Patent Application
20070292008
This nonprovisional application is based on Japanese Patent Application No. 2006-153826 filed with the Japan Patent Office on Jun. 1, 2006 the entire contents of which are hereby incorporated by reference.
1. Field of the Invention
The present invention relates to an image comparing apparatus, and particularly to an image comparing apparatus detecting a comparison-untargeted element in an image to be compared.
2. Description of the Background Art
There are increasing tendencies to employ a technology of authenticating personal identification, e.g., for giving permission of use of devices to users. In many cases, the personal authentication is performed using bodily features of individuals and, for example, fingerprints are used in view of easy collection. For the authentication using the fingerprint, image comparison is performed using a fingerprint image that is read from a user's finger placed on a fingerprint read surface of a fingerprint sensor.
When the fingerprint read surface is stained in the fingerprint authenticating operation, the fingerprint image contains noise components due to such smear so that the correct image comparison cannot be performed. Japanese Patent Laying-Open No. 62-197878 has disclosed a method for overcoming the above disadvantage.
In this publication, a fingerprint comparing apparatus captures an image of a finger table or plate before a finger is placed thereon, detects a contrast of a whole image thus captured and determines whether the finger table is stained or not, based on whether a detected contrast value exceeds a predetermined value or not. When the apparatus detects that the contrast value exceeds the predetermined value, it issues an alarm. When the alarm is issued, a user must clean the finger table and then must place the finger thereon again for image capturing. This results in low operability.
According to the above publication, the user is required to remove any smear that is detected on the finger table prior to the fingerprint comparison, resulting in inconvenience. Further, the processing is configured to detect any smear based on image information about the whole finger table. Therefore, even when the position and/or the size of the smear do not interfere with actual fingerprint comparison, the user is required to clean the table and to perform the operation of capturing the fingerprint image again. Therefore, the comparison processing takes a long time, and imposes inconvenience on the users.
Accordingly, an object of the invention is to provide an image comparing apparatus that can efficiently perform image comparison.
For achieving the above object, an image comparing apparatus according to an aspect of the invention includes an element detecting unit detecting an element not to be used for comparison in an image; a comparison processing unit performing comparison using the image not including the element detected by the element detecting unit; and a feature value detecting unit detecting and providing a feature value according to a pattern of a partial image corresponding to each of the partial images in the image. The element detecting unit detects, as the above element, a region indicated by a combination of the partial images having predetermined feature values provided from the feature value detecting unit.
Preferably, the image is an image of a fingerprint. The feature values provided from the feature value detecting unit is classified as a value indicating that the pattern of the partial image extends in a vertical direction of the fingerprint, a value indicating that the pattern of the partial image extends in a horizontal direction of the fingerprint or a value indicating otherwise.
Preferably, the image is an image of a fingerprint. The feature value provided from the feature value detecting unit is classified as a value indicating that the pattern of the partial image extends in an obliquely rightward direction of the fingerprint, a value indicating that the pattern of the partial image extends in an obliquely leftward direction of the fingerprint or a value indicating otherwise.
Preferably, the predetermined feature value is one of the other values.
Preferably, the combination is a combination of the plurality of partial images having the feature values classified as the otherwise values and neighboring to each other in a predetermined direction.
Preferably, the comparison processing unit includes a position searching unit searching first and second images to be compared, and searching a position of a region indicating a maximum score of matching with a partial region of the first image in the partial regions not including a region of the element detected by the element detecting unit in the second image, a similarity score calculating unit calculating and providing a score of similarity between the first and second images based on a positional relationship quantity indicating a relationship between a reference position for measuring the position of the region in the first image and a position of a maximum matching score searched by the position searching unit, and a determining unit determining, based on the provided similarity score, whether the first and second images match each other or not.
Preferably, the position searching unit searches the maximum matching score position in each of the partial images in the partial regions of the second image not including the region of the element detected by the element detecting unit, and the similarity score calculating unit provides, as the similarity score, the number of the partial images exhibiting such a relationship that a quantity of the positional relationship between the maximum matching score position searched by the position searching unit and the reference position in the partial image of the second image is smaller than a predetermined quantity.
Preferably, the positional relationship quantity indicates a direction and a distance of the maximum matching score position with respect to the reference position.
Preferably, a sum of the maximum matching scores obtained from the partial images having the positional relationship quantity smaller than the predetermined quantity is provided as the similarity score.
Preferably, the image comparing apparatus further includes an image input unit for inputting the image, and a registered image storage storing images of a plurality of the preregistered partial images. The partial images of the first image are read from the registered image storage, and the other image is input by the image input unit.
Preferably, the image input unit has a read surface bearing a target for reading an image of the target placed on the read surface.
According to another aspect of the invention, an image comparing method using a computer for comparing an image includes the steps of: detecting an element not to be used for comparison in the image; performing comparison using the image not including the element detected by the step of the element detection; and detecting and providing a feature value according to a pattern of a partial image corresponding to each of the partial images in the image. The step of detecting the element detects, as the element, a region indicated by a combination of the partial images having predetermined feature values provided from the step of detecting the feature value.
According to still another aspect, the invention provides an image comparison program for causing a computer to execute the above image comparison method.
According to yet another aspect, the invention provides a computer-readable record medium bearing an image comparison program for causing a computer to execute the above image comparison method.
According to the invention, the feature value according to the pattern of each of the plurality of partial images is detected corresponding to each partial image in the comparison target image, and thereby the element that is the region indicated by the combination of the partial images having the predetermined feature values. The comparison processing is performed using the images from which the detected elements are removed.
Since the elements to be untargeted for comparison are detected and the comparison is performed on the images not including the detected elements, the image comparison can be continued without an interruption even when the image contains the element that cannot be compared due to noise components such as smear. Accordingly, it is possible to increase the number of images compared per time, and to achieve high comparison processing efficiency.
The foregoing and other objects, features, aspects and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings.
Embodiments of the invention will now be described with reference to the drawings.
The computer may be provided with a magnetic tape drive for accessing a magnetic tape of a cassette type that is removably loaded thereinto.
Referring to
Image input unit 101 includes a fingerprint sensor 100, and provides fingerprint image data corresponding to the fingerprint read by fingerprint sensor 100. Fingerprint sensor 100 may be of any one of optical, pressure and capacitance types.
Memory 102 stores image data and various calculation results. Reference memory 1021 stores a plurality of partial images of template fingerprint images (i.e., fingerprint images for templates). Calculation memory 1022 stores various calculation results and the like. Captured image memory 1023 stores fingerprint image data provided from image input unit 101. Reference image feature value memory 1024 and captured image feature value memory 1025 store results of calculation performed by feature value calculate unit 1045 to be described later. Bus 103 is used for transferring control signals and data signals between various units.
Image correcting unit 104 corrects a density in the fingerprint image data provided from image input unit 101.
Feature value calculate unit 1045 performs the calculation for each of the images in a plurality of partial regions set in the image, and obtains a value corresponding to a pattern represented by the partial image. Feature value calculate unit 1045 provides, as a partial image feature value, the result of this calculation corresponding to the reference memory to reference image feature value memory 1024, and provides the result of the calculation corresponding to the captured image memory to captured image feature value memory 1025.
In the operation of determining the comparison-untargeted image element, element determining unit 1047 refers to captured image feature value memory 1025, and performs the determinations about the comparison-untargeted image element according to the combination of the feature values of partial images in specific portions of the image.
Maximum matching score position searching unit 105 is similar to a so-called template matching portion. More specifically, it restricts the comparison-targeted partial image with reference to determination information calculated by element determining unit 1047. Further, maximum matching score position searching unit 105 reduces a search range according to the partial image feature values calculated by feature value calculate unit 1045. Then, maximum matching score position searching unit 105 uses a plurality of partial regions in one of the fingerprint images as the template, and finds a position achieving the highest score of matching between this template and the other fingerprint image.
Similarity score calculate unit 106 calculates the similarity score based on a movement vector to be described later, using the result information of maximum matching score position searching unit 105 stored in memory 102. Comparison determining unit 107 determines the matching or mismatching based on the similarity score calculated by similarity score calculate unit 106. Control unit 108 controls the processing by the various units in comparison processing unit 11.
For comparison between the two fingerprint images, image comparing apparatus 1 shown in
First, control unit 108 transmits a signal for starting the image input to image input unit 101, and then waits for reception of an image input end signal. Image input unit 101 performs the input of image “A” to be compared, and stores input image “A” via bus 103 at a predetermined address in memory 102 (step T1). In this embodiment, input image “A” is stored at the predetermined address in reference memory 1021. After the input of image “A”, image input unit 101 transmits the image input end signal to control unit 108.
After control unit 108 receives the image input end signal, it transmits the image input start signal to image input unit 101 again, and then waits for reception of the image input end signal. Image input unit 101 performs the input of an image “B” to be compared, and stores input image “B” via bus 103 at a predetermined address in memory 102 (step T1). In this embodiment, image “B” is stored at a predetermined address in captured image memory 1023. After the input of image “B”, image input unit 101 transmits the image input end signal to control unit 108.
Then, control unit 108 transmits an image correction start signal to image correcting unit 104, and then waits for reception of an image correction end signal. In many cases, density values of respective pixels and a whole density distribution of input images vary depending on characteristics of image input unit 101, a degree of dryness of a skin and a pressure of a placed finger, and therefore image qualities of the input images are not uniform. Accordingly, it is not appropriate to use the image data for the comparison as it is. Image correcting unit 104 corrects the image quality of the input image to suppress variations in conditions at the time of image input (step T2). More specifically, processing such as flattening of histogram (“Computer GAZOU SHORI NYUMON (Introduction to computer image processing)”, SOKEN SHUPPAN, p. 98) or image thresholding or binarization (“Computer GAZOU SHORI NYUMON (Introduction to computer image processing)”, SOKEN SHUPPAN, pp. 66-69) is performed on the whole image corresponding to the input image data or each of small divided regions of the image, and more specifically, is performed on images “A” and “B” stored in memory 102, i.e., in reference memory 1021 and captured image memory 1023.
After image correcting unit 104 completes the image correction of images “A” and “B”, it transmits the image correction end signal to control unit 108.
Thereafter, feature value calculate unit 1045 calculates the feature values of the partial images of the image subjected to the image correction by image correcting unit 104 (step T25a). Thereafter, element determining unit 1047 performs the determination about the comparison-untargeted image elements (step T25b). Then, similarity score calculate unit 106 performs the calculation of the similarity score, and comparison determining unit 107 performs comparison determination (step T3). Printer 690 or display 610 outputs the result of such comparison determination (step T4). Processing in steps T25a, T25b and T3 will be described later in greater detail.
(Calculation of Partial Image Feature Value)
Then, description will be given on steps of calculating the feature value of the partial image in step T25a.
<Three Kinds of Feature Values>
Description will now be given on the case where three kinds of feature values are employed.
The partial image feature value calculation in the first embodiment is performed to obtain, as the partial image feature value, a value corresponding to the pattern of the calculation target partial image. More specifically, processing is performed to detect maximum numbers “maxhlen” and “maxvlen” of black pixels that continue to each other in the horizontal and vertical directions, respectively. Maximum continuous black pixel number “maxhlen” in the horizontal direction indicates a magnitude or degree of tendency that the pattern extends in the horizontal direction (i.e., it forms a lateral stripe), and maximum continuous black pixel number “maxvlen” in the vertical direction indicates a magnitude or degree of tendency that the pattern extends in the vertical direction (i.e., it forms a longitudinal stripe). These values “maxhlen” and “maxvlen” are compared with each other. When it is determined from the comparison that this pixel number in the horizontal direction is larger than the others, “H” indicating the horizontal direction (lateral stripe) is output. When the determined pixel number in the vertical direction is larger than the others, “V” indicating the vertical direction (longitudinal stripe) is output. Otherwise, “X” is output.
Referring to
However, even when the result of the determination is “H” or “V”, it may be determined that neither of maximum continuous black pixel number “maxhlen” and “maxvlen” is smaller than a corresponding lower limit “hlen0” or “vlen0” that is predetermined for the corresponding direction. In this case, “X” is output. These conditions can be expressed as follows. When (maxhlen>maxvlen and maxhlen≧hlen0) is satisfied, “H” is output. When (maxvlen>maxhlen and maxvlen≧vlen0) is satisfied, “V” is output. Otherwise, “X” is output.
First, control unit 108 transmits a calculation start signal for the partial image feature values to feature value calculate unit 1045, and then waits for reception of a calculation end signal for the partial image feature values. Feature value calculate unit 1045 reads partial images “Ri” of the calculation target images from reference memory 1021 and captured image memory 1023, and temporarily stores them in calculation memory 1022 (step S1). Feature value calculate unit 1045 reads stored partial image “Ri”, and obtains maximum continuous black pixel numbers “maxhlen” and “maxvlen” in the horizontal and vertical directions (step S2). Processing of obtaining maximum continuous black pixel numbers “maxhlen” and “maxvlen” in the horizontal and vertical directions will now be described with reference to
Then, the value of pixel count “j” in the vertical direction is compared with the value of a variable “n” indicating the maximum pixel number in the vertical direction (step SH002). When (J=>n) is satisfied, step SH016 is executed. Otherwise, step SH003 is executed. In the first embodiment, “n” is equal to 16, and “j” is equal to 0 at the start of the processing so that the process proceeds to step SH003.
In step SH003, processing is performed to initialize a pixel count “i” in the horizontal direction, last pixel value “c”, current continuous pixel value “len” and maximum continuous black pixel number “max” in the current row to attain (i=0, c=0, len=0 and max=0) in step SH003. Then, pixel count “i” in the horizontal direction is compared with maximum pixel number “m” in the horizontal direction (step SH004). When (i≧m) is satisfied, processing in step SH011 is executed, and otherwise next step SH005 is executed. In the first embodiment, “m” is equal to 16, and “i” is equal to 0 at the start of the processing so that the process proceeds to step SH005.
In step SH005, last pixel value “c” is compared with a current comparison target, i.e., a pixel value “pixel (i, j)” at coordinates (i, j). In the first embodiment, “c” is already initialized to 0 (white pixel), and “pixel (0, 0)” is 0 (white pixel) with reference to
In step SH006, (len=len+1) is executed. In the first embodiment, since “len” is already initialized to 0, it becomes 1 when 1 is added thereto. Then, the process proceeds to step SH010.
In step SH010, the pixel count in the horizontal direction is incremented by one (i.e., i=i+1). Since “i” is already initialized to 0 (i=0), it becomes 1 when 1 is added thereto (i=1). Then, the process returns to step SH004. Thereafter, all the pixels “pixel (i, 0)” in the 0th row are white and take values of 0 as illustrated in
In step SH011, when (c=1 and max<len) are satisfied, step SH012 is executed, and otherwise step SH013 is executed. At this point in time, “c” is 0, “len” is 15 and “max” is 0 so that the process proceeds to step SH013.
In step SH013, maximum continuous black pixel number “maxhlen” in the horizontal direction that are already obtained from the last and preceding rows are compared with maximum continuous black pixel number “max” in the current row. When (maxhlen<max) is attained, processing is executed in step SH014, and otherwise processing in step SH015 is executed. Since “maxhlen” and “max” are currently equal to 0, the process proceeds to step SH015.
In step SH015, (j=j+1) is executed, and thus pixel count “j” in the vertical direction is incremented by one. Since “j” is currently equal to 0, “j” becomes 1, and the process returns to step SH002.
Thereafter, the processing in steps SH002-SH015 are similarly repeated for “j” from 1 to 15. When “j” becomes 16 after the processing in step SH015, the processing in next step SH002 is performed to compare the value of pixel count “j” in the vertical direction with the value of maximum pixel number “n” in the vertical direction. When the result of this comparison is (j≧n), step SH016 is executed, and otherwise step SH003 is executed. Since “j” and “n” are currently 16, the process proceeds to step SH016.
In step SH016, “maxhlen” is output. According to the description already given and
Description will now be given on a flowchart of the processing (step S2) of obtaining maximum continuous black pixel number “maxvlen” in the vertical direction. This processing is performed in the processing (Step T2a) of calculating the partial image feature value according to the first embodiment of the invention. Since it is apparent that the processing in steps SV001-SV016 in
The subsequent processing performed with reference to “maxhlen” and “maxvlen” provided in the foregoing steps will now be described in connection with the processing in and after step S3 in
In step S3, “maxhlen” is compared with “maxvlen” and predetermined lower limit “hlen0” of the maximum continuous black pixel number. When it is determined that the conditions of (maxhlen>maxvlen and maxhlen≧hlen0) are satisfied (YES in step S3), step S7 is executed. Otherwise (NO in step S3), step S4 is executed. Assuming that “maxhlen” is 14, “maxvlen” is 4 and lower limit “hlen0” is 2 in the current state, the above conditions are satisfied so that the process proceeds to step S7. In step S7, “H” is stored in reference image feature value memory 1024 or in the feature value storage region for partial image “Ri” corresponding to the original image in captured image feature value memory 1025, and the calculation end signal for the partial image feature value is transmitted to control unit 108.
Assuming that lower limit “hlen0” is 15, it is determined that the conditions are not satisfied in step S3, and the process proceeds to step S4. In step S4, it is determined whether the conditions of (maxvlen>maxhlen and maxvlen≧vlen0) are satisfied or not. When satisfied (YES in step S4), the processing in step S5 is executed. Otherwise, the processing in step S6 is executed.
Assuming that “maxhlen” is 15, “maxvlen” is 4 and “vlen0” is 5, the above conditions are not satisfied so that the process proceeds to step S6. In step S6, “X” is stored in reference image feature value memory 1024 or in the feature value storage region for partial image “Ri” corresponding to the original image in captured image feature value memory 1025, and transmits the calculation end signal for the partial image feature value to control unit 108.
Assuming that the output values exhibit the relationships of (maxhlen=4, maxvlen=10, hlen0=2 and vlen=12), the conditions in step S3 are not satisfied, and further the conditions in step S4 are not satisfied so that the personal computer in step S5 is executed. In step S5, “V” is stored in reference image feature value memory 1024 or in the feature value storage region for partial image “Ri” corresponding to the original image in captured image feature value memory 1025, and the calculation end signal for the partial image feature value is transmitted to control unit 108.
As described above, feature value calculate unit 1045 in the first embodiment of the invention extracts (i.e., specifies) the pixel rows and columns in the horizontal and vertical directions from partial image “Ri” (see
<Another Example of Three Kinds of Feature Values>
Another example of the three kinds of partial image feature values will be described. Calculation of the partial image feature values is schematically described below according to
In this example, the processing is performed to obtain an increase (i.e., a quantity of increase) “hcnt” by which the black pixels are increased in number when calculation target partial image “Ri” is shifted leftward and rightward by one pixel as illustrated in
The increase of the black pixels caused by shifting the image leftward and rightward by one pixel as illustrated in
The increase of the black pixels caused by shifting the image upward and downward by one pixel as illustrated in
In the above case, when the black pixels of certain pixels overlap together, the black pixel is formed. When the white and block pixels overlap together, the black pixel is formed. When the while pixels overlap together, the white pixel is formed.
Details of the calculation of the partial image feature value will be described below according to the flowchart of
First, control unit 108 transmits the calculation start signal for the partial image feature value to feature value calculate unit 1045, and then waits for reception of the calculation end signal for the partial image feature values.
Feature value calculate unit 1045 reads partial images “Ri” (see
The processing for obtaining increases “hcnt” and “vcnt” will now be described with reference to
Referring to
In step SHT03, feature value calculate unit 1045 initializes pixel count “i” in the horizontal direction to zero (i=0). Then, feature value calculate unit 1045 compares pixel count “i” in the horizontal direction with maximum pixel number “m” in the horizontal direction (step SHT04). When the comparison result is (i≧m), step SHT05 is executed. Otherwise, next step SHT06 is executed. Since “m” is equal to 16, and “j” is equal to 0 at the start of the processing, the process proceeds to step SHT06.
In step SHT06, partial image “Ri” is read, and it is determined whether the current comparison target, i.e., pixel value pixel (i, j) at coordinates (i, j) is 1 (black pixel) or not, whether pixel value pixel (i−1, j) at coordinates (i−1, j) shifted left by one from coordinates (i, j) is 1 or not, or whether pixel value pixel (i+1, j) at coordinates (i+1, j) shifted right by one from coordinates (i, j) is one or not. When (pixel (i, j)=1, pixel (i−1, j)=1 or pixel (i+1, j)=1) is attained, step SH08 is executed. Otherwise, step SHT07 is executed.
In a range defined by pixels shifted horizontally or vertically by one pixel from partial image “Ri”, i.e., in a range defined of pixels of Ri(−1, −m+1, −1), Ri(−1, −1−n+1), Ri(m+1, −1−n+1) and Ri(−1−m+1, n+1), it is assumed that the pixels take the values of (and are white) as illustrated in
In step SHT07, pixel value work (i, j) at coordinates (i, j) of image “WHi” stored in calculation memory 1022 is set to 0. This image “WHi” is prepared by overlaying, on the original image, images prepared by shifting partial image “Ri” leftward and rightward by one pixel (see
In step SHT09, (i=i+1) is attained, and thus horizontal pixel count “i” is incremented by one. Since “i” was initialized to 0, “i” becomes one after addition of one. Then, the process returns to step SHT04. Thereafter, all the pixel values pixel (i, j) in the 0th row are 0 (white pixel) as illustrated in
In step SHT05, (j=j+1) is performed. Thus, vertical pixel count “j” is incremented by one. Since “j” was equal to 0, “j” becomes 1, and the process returns to step SHT02. Since the processing on a new row starts, the process proceeds to steps SHT03 and SHT04, similarly to the 0th row. Thereafter, processing in steps SHT04-SHT09 will be repeated until (pixel (i, j)=1) is attained, i.e., the pixel in the first row and fourteenth column (i=14 and j=1) is processed. After the processing in step SHT09, (i=14) is attained. Since the state of (m=16 and i=14) is attained, the process proceeds to step SHT06.
In step SHT06, (pixel (i+1, j)=1), i.e., (pixel (14+1, 1)=1) is attained so that the process proceeds to step SHT08.
In step SHT08, pixel value work (i, j) at coordinates (i, j) of image “WHi” stored in calculation memory 1022 is set to one. This image “WHi” is prepared by overlaying, on the original image, images prepared by shifting partial image “Ri” leftward and rightward by one pixel (see
The process proceeds to step SHT09. “i” becomes equal to 16 and the process proceeds to step SHT04. Since the state of (m=16 and i=16) is attained, the process proceeds to step SHT05, “j” becomes equal to 2 and the process proceeds to step SHT02. Thereafter, the processing in steps SHT02-SHT09 is repeated similarly for j=2−15. When “j” becomes equal to 16 after the processing in step SHT09, the processing is performed in step SHT02 to compare the value of vertical pixel count “J” with vertical maximum pixel number “n”. When the result of comparison indicates (j≧n), the processing in step SHT10 is executed. Otherwise, the processing in step SHT03 is executed. Since the state of (j=16 and n=16) is currently attained, the process proceeds to step SHT10. At this time, calculation memory 1022 has stored image “WHi” prepared by overlaying, on partial image “Ri” to be currently compared for comparison, images prepared by shifting partial image “Ri” leftward and rightward by one pixel.
In step SHT10, calculation is performed to obtain a difference “cnt” between pixel value work (i, j) of image “WHi” stored in calculation memory 1022 and prepared by overlaying images shifted leftward and rightward by one pixel and pixel value pixel (i, j) of partial image “Ri” that is currently compared for comparison. The processing of calculating difference “cnt” between “work” and “pixel” will now be described with reference to
Since “n” is equal to 16, and “j” is equal to 0 at the start of the processing, the process proceeds to step SC003. In step SC003, horizontal pixel count “i” is initialized to 0. Then, horizontal pixel count “i” is compared with horizontal maximum pixel number “m” (step SC004). When (i≧m) is attained, the processing in step SC005 is executed, and otherwise the processing in step SC006 is executed. Since “m” is equal to 16, and “i” is equal to 0 at the start of the processing, the process proceeds to step SC006.
In step SC006, it is determined whether pixel value pixel (i, j) of the current comparison target, i.e., partial image “Ri” at coordinates (i, j) is 0 (white pixel) or not, and pixel value work (i, j) of image “WHi” prepared by one-pixel shifting is 1 (black pixel) or not. When (pixel (i, j)=0 and work (i, j)=1) is attained, the processing in step SC007 is executed. Otherwise, the processing in step SC008 is executed. Referring to
In step SC008, horizontal pixel count “i” is incremented by one (i.e., i=i+1). Since i was initialized to 0, it becomes 1 when 1 is added thereto. Then, the process returns to step SC004. Referring to
In step SC005, vertical pixel count “j” is incremented by one (j=j+1). Since “j” was equal to 0, “j” becomes equal to 1, and the process returns to step SC002. Since a new row starts, the processing is performed in steps SC003 and SC004, similarly to the 0th row. Thereafter, the processing in steps SC004-SC008 is repeated until the state of (i=15 and j=1) is attained, i.e., until the processing of the pixel in the first row and fourteenth column exhibiting the state of (pixel (i, j)=0 and work (i, j)=1) is completed. After the processing in step SC008, “i” is equal to 15. Since the state of (m=16 and i=15) is attained, the process proceeds to step SC006.
In step SC006, pixel (i, j) is 0 and work (i, j) is 1, i.e., pixel (14, 1) is 0 and work (14, 1) is 1 so that the process proceeds to step SC007.
In step SC007, differential count “cnt” is incremented by one (cnt=cnt+1). Since count “cnt” was initialized to 0, it becomes 1 when 1 is added. The process proceeds to step SC008, and the process will proceed to step SC004 when “i” becomes 16. Since (m=16 and i=16) is attained, the process proceeds to step SC005, and will proceed to step SC002 when (j=2) is attained.
Thereafter, the processing in steps SC002-SC009 is repeated for j=2−15 in a similar manner. When (0=15) is attained after the processing in step SC008, vertical pixel count “j” is compared with vertical maximum pixel number “n” in step SC002. When the comparison result indicates (j≧n), the process returns to the steps in the flowchart of
In step SHT11, the operation of (hcnt=cnt) is performed, and thus difference “cnt” calculated according to the flowchart of
In the feature value calculation processing (step T2a) in
A value of 96 is output as increase “vcnt” caused by the upward and downward shifting. This value of 96 is the difference between image “WVi” obtained by upward and downward one-pixel-shifting and overlapping in
Output increases “hcnt” and “vcnt” are then processed in and after step ST3 in
In step ST3, “hcnt”, “vcnt” and lower limit “vcnt0” of the increase in maximum black pixel number in the vertical direction are compared. When the conditions of (vcnt>2×hcnt, and vcnt≧vcnt0) are satisfied, the processing in step ST7 is executed. Otherwise, the processing in step ST4 is executed. The state of (vcnt=96 and hcnt=21) is currently attained, and the process proceeds to step ST7, assuming that “vcnt” is equal to 4. In step ST7, “H” is stored in reference image feature value memory 1024 or in the feature value storage region for partial image “Ri” corresponding to the original image in captured image feature value memory 1025, and the calculation end signal for the partial image feature value is transmitted to control unit 108.
Assuming that the output values of (vcnt=30 and hcnt=20) are output in step ST2 and (vcnt0=4) is attained, the conditions in step ST3 are not satisfied, and the process proceeds to step ST4. When it is determined in step ST4 that the conditions of (hcnt>2×vcnt and hcnt≧hcnt0) are satisfied, the processing in step ST5 is executed. Otherwise, the processing in step ST6 is executed.
In this case, the process proceeds to step ST6, in which “X” is stored in reference image feature value memory 1024 or in the feature value storage region for partial image “Ri” corresponding to the original image in captured image feature value memory 1025, and the calculation end signal for the partial image feature value is transmitted to control unit 108.
Assuming that the values of (vcnt=30 and hcnt=70) are output in step ST2 and (hcnt0=4) is attained, it is determined that the conditions of (vcnt>2×hcnt, and vcnt≧vcnt0) are not satisfied in step ST3, and the process proceeds to step ST4. It is determined in step ST4 whether the conditions of (hcnt>2×vcnt, and hcnt≧hcnt0) are satisfied or not. When satisfied, the processing in step ST5 is executed. Otherwise, the processing in step ST6 is executed.
In this state, the above conditions are satisfied. Therefore, the process proceeds to step ST5. “V” is stored in reference image feature value memory 1024 or in the feature value storage region for partial image “Ri” corresponding to the original image in captured image feature value memory 1025, and the calculation end signal for the partial image feature value is transmitted to control unit 108.
The above calculation of the feature values of the partial image has the following feature. Reference image “A” or captured image “B” may contain noises. For example, the fingerprint image may be partially lost due to wrinkles in the finger or the like. Thereby, as shown in
As described above, feature value calculate unit 1045 obtains image “WHi” by shifting partial image “Ri” leftward and rightward by a predetermined number of pixel(s), and also obtains image “WVi” by shifting it upward and downward by a predetermined number of pixel(s). Further, feature value calculate unit 1045 obtains increase “hcnt” in number of the black pixels that is the difference between partial image “Ri” and image “WHi” obtained by shifting it leftward and rightward by one pixel, and obtains increase “hcnt” in number of the black pixels that is the difference between partial image “Ri” and image “WVi” obtained by shifting it upward and downward by one pixel. Based on these increases, feature value calculate unit 1045 determines whether the pattern of partial image “Ri” tends to extend horizontally (e.g., to form a lateral stripe), to extent vertically (e.g., to form a longitudinal stripe) or to extend neither vertically nor horizontally. Feature value calculate unit 1045 outputs a value (“H”, “V” or “X”) according to the result of this determination. This output value indicates the feature value of partial image “Ri”.
<Still Another Example of Three Kinds of Feature Values>
The three kinds of partial image feature values are not restricted to those already described, and may be as follows. The calculation of the partial image feature value is schematically described below according to
The increase of the black pixels caused by shifting the image obliquely rightward represents the following difference. Assuming that (i, j) represents the coordinate of each pixel in the original image of 16 by 16 pixels, an image is prepared by shifting the original image to change the coordinate (i, j) of each pixel to (i+1, j−1), and another image is also prepared by shifting the original image to change the coordinate (i, j) of each pixel to (i−1, j+1). The two images thus formed are overlaid on the original image to prepare the overlapping image (16 by 16 pixels) such that the pixels at the same coordinates (i, j) match together. The foregoing increase indicates the difference in total number of the black pixels between the overlapping image thus formed and the original image.
The increase of the black pixels caused by shifting the image obliquely leftward represents the following difference. Assuming that (i, j) represents the coordinate of each pixel in the original image of 16 by 16 pixels, an image is prepared by shifting the original image to change the coordinate (i, j) of each pixel to (i−1, j−1), and another image is also prepared by shifting the original image to change the coordinate (i, j) of each pixel to (i+1, j−1). The two images thus formed are overlaid on the original image to prepare the overlapping image (16 by 16 pixels) such that the pixels at the same coordinates (i, j) match together. The foregoing increase indicates the difference in total number of the black pixels between the overlapping image thus formed and the original image.
In this case, when the black pixels of certain pixels overlap together, the black pixel is formed. When the white and block pixels overlap together, the black pixel is formed. When the while pixels overlap together, the white pixel is formed.
However, even when it is determined to output “R” or “L”, “X” will be output when the increase of the black pixels is smaller than the lower limit value “lcnt0” and “rcnt0” that are preset for the opposite directions, respectively. This can be expressed by the conditional equations as follows. When (1) lcnc>2×rcnt and (2) lcnt≧lcnt0 are attained, “R” is output. When (3) rcnt>2×lcnt and (4) rcnt≧rcnt0 are attained, “L” is output. Otherwise, “X” is output.
Although “R” indicating the obliquely rightward direction is output when increase “lcnt” is larger than double increase “rcnt”, the threshold, i.e., double the value may be changed to another value. This is true also with respect to the obliquely leftward direction. In some cases, it is known in advance that the number of black pixels in the partial image falls within a certain range (e.g., 30%-70% of the whole pixel number in partial image “Ri”), and that the image can be appropriately used for the comparison. In these cases, the above conditional equations (2) and (4) may be eliminated.
First, control unit 108 transmits the calculation start signal for the partial image feature value to feature value calculate unit 1045, and then waits for reception of the calculation end signal for the partial image feature values.
Feature value calculate unit 1045 reads partial images “Ri” (see
The processing for obtaining increases “rcnt” and “lcnt” will now be described with reference to
Referring to
In step SR03, feature value calculate unit 1045 initializes pixel count “i” in the horizontal direction to zero (i=0). Then, feature value calculate unit 1045 compares pixel count “i” in the horizontal direction with maximum pixel number “m” in the horizontal direction (step SR04). When the comparison result is (i≧m), step SR05 is executed. Otherwise, next step SR06 is executed. Since “m” is equal to 16, and “j” is equal to 0 at the start of the processing, the process proceeds to step SR06.
In step SR06, partial image “Ri” is read, and it is determined whether the current comparison target, i.e., pixel value pixel (i, j) at coordinates (i, j) is 1 (black pixel) or not, whether pixel value pixel (i+1, j+1) at coordinates (i+1, j+1) shifted toward the upper right by one from coordinates (i, j) is 1 or not, or whether pixel value pixel (i+1, j−1) at coordinates (i+1, j−1) shifted obliquely rightward by one from coordinates (i, j) is 1 or not. When (pixel (i, j)=1, pixel (i+1, j+1)=1 or pixel (i+1, j−1)=1) is attained, step SR8 is executed. Otherwise, step SR07 is executed.
In a range defined by pixels shifted horizontally or vertically by one pixel from partial image “Ri”, i.e., in a range defined by pixels of Ri(−1−m+1, −1), Ri(−1, −1−n+1), Ri(m+1, −1−n+1) and Ri(−1−m+1, n+1), it is assumed that the pixels take the values of 0 (and are white) as illustrated in
In step SR07, pixel value work (i, j) at coordinates (i, j) of image “WHi” stored in calculation memory 1022 is set to 0. This image “WVHi” is prepared by overlaying, on the original image, the images shifted obliquely rightward by one pixel (see
In step SR09, (i=i+1) is attained, and thus horizontal pixel count “i” is incremented by one. Since “i” was initialized to 0, “i” becomes 1 when 1 is added thereto. Then, the process returns to step SR04.
In step SR05, (j=j+1) is performed. Thus, vertical pixel count “j” is incremented by one. Since “j” was equal to 0, “j” becomes 1, and the process returns to step SR02. Since the processing on a new row starts, the process proceeds to steps SR03 and SR04, similarly to the 0th row. Thereafter, processing in steps SR04-SR09 will be repeated until (pixel (i, j)=1) is attained, i.e., the pixel in the first row and fifth column (i=5 and j=1) is processed. After the processing in step SR09, (i=5) is attained. Since the state of (m=16 and i=5) is attained, the process proceeds to step SR06.
In step SR06, (pixel (i, j)=1), i.e., (pixel (5, 1)=1) is attained so that the process proceeds to step SR08.
In step SR08, pixel value work (i, j) at coordinates (i, j) of image “WRi” stored in calculation memory 1022 is set to one.
The process proceeds to step SR09. “i” becomes equal to 16 and the process proceeds to step SR04. Since the state of (m=16 and i=16) is attained, the process proceeds to step SR05, “j” becomes equal to 2 and the process proceeds to step SR02. Thereafter, the processing in steps SR02-SR09 is repeated similarly for j=2−15. When “j” becomes equal to 16 after the processing in step SR09, the processing is performed in step SR02 to compare the value of vertical pixel count “J” with vertical maximum pixel number “n”. When the result of comparison indicates (j≧n), the processing in step SR10 is executed. Otherwise, the processing in step SR03 is executed. Since the state of (j=16 and n=16) is currently attained, the process proceeds to step SR10. At this time, calculation memory 1022 has stored image “WRi” prepared by overlaying, on partial image “Ri” to be currently compared for comparison, images prepared by shifting partial image “Ri” obliquely rightward by one pixel.
In step SR 10, calculation is performed to obtain difference “cnt” between pixel value work (i, j) of image “WRi” stored in calculation memory 1022 and prepared by overlaying images shifted obliquely rightward by one pixel and pixel value pixel (i, j) of partial image “Ri” that is currently compared for comparison. The processing of calculating difference “cnt” between “work” and “pixel” will now be described with reference to
Since “n” is equal to 16, and “j” is equal to 0 at the start of the processing, the process proceeds to step SN003. In step SN003, horizontal pixel count “i” is initialized to 0. Then, horizontal pixel count “i” is compared with horizontal maximum pixel number “m” (step SN004). When the comparison result indicates (i≧m), the processing in step SN005 is executed, and otherwise the processing in step SN006 is executed. Since “m” is equal to 16, and “i” is equal to 0 at the start of the processing, the process proceeds to step SN006.
In step SN006, it is determined whether pixel value pixel (i, j) of the current comparison target, i.e., partial image “Ri” at coordinates (i, j) is 0 (white pixel) or not, and pixel value work (i, j) of image “WRi” prepared by one-pixel shifting is 1(black pixel) or not. When (pixel (i, j)=0 and work (i, j)=1) is attained, the processing in step SN007 is executed. Otherwise, the processing in step SN008 is executed. Referring to
In step SN008, horizontal pixel count “i” is incremented by one (i.e., i=i+1). Since i was initialized to 0, it becomes 1 when 1 is added thereto. Then, the process returns to step SN004. The processing in steps SN004-SN008 is repeated until (i=15) is attained. When “i” becomes equal to 16 after the processing in step SN008, the process proceeds to step SN004. Since the state of (m=16 and i=16) is attained, the process proceeds to step SN005.
In step SN005, vertical pixel count “j” is incremented by one (j=j+1). Since “j” was equal to 0, “j” becomes equal to 1, and the process returns to step SN002. Since a new row starts, the processing is performed in steps SN003 and SN004, similarly to the 0th row. Thereafter, the processing in steps SN004-SN008 is repeated until the state of (i=10 and j=1) is attained, i.e., until the processing of the pixel in the first row and eleventh column exhibiting the state of (pixel (i, j)=0 and work (i, j)=1) is completed. After the processing in step SN008, “i” is equal to 10. Since the state of (m=16 and i=10) is attained, the process proceeds to step SN006.
In step SN006, pixel (i, j) is 0 and work (i, j) is 1, i.e., pixel (10, 1) is 0 and work (10,) is 1 so that the process proceeds to step SN007.
In step SN007, differential count “cnt” is incremented by one (cnt=cnt+1). Since count “cnt” was initialized to 0, it becomes 1 when 1 is added. The process proceeds to step SN008, and the process will proceed to step SN004 when “i” becomes 16. Since (m=16 and i=16) is attained, the process proceeds to step SN005, and will proceed to step SN002 when (j=2) is attained.
Thereafter, the processing in steps SN002-SN009 is repeated for j=2−15 in a similar manner. When (=16) is attained after the processing in step SN008, vertical pixel count “j” is compared with vertical maximum pixel number “n” in step SN002. When the comparison result indicates (j≧n), the process returns to the steps in the flowchart of
In step SR11, the operation of (rcnt=cnt) is performed, and thus difference “cnt” calculated according to the flowchart of
In the feature value calculation processing (step T2a) in
A value of 115 is output as increase “lcnt” caused by the obliquely leftward shifting. This value of 115 is the difference between image “WLi” obtained by obliquely leftward one-pixel shifting and overlapping in
Output increases “rcnt” and “lcnt” are then processed in and after step SM3 in
In step SM3, “rcnt”, “lcnt” and lower limit “vlcnt0” of the increase in maximum black pixel number in the obliquely leftward direction are compared. When the conditions of (lcnt>2×rcnt, and lcnt≧lcnt0) are satisfied, the processing in step SM7 is executed. Otherwise, the processing in step SM4 is executed. The state of (lcnt=115 and rcnt=21) is currently attained, and the process proceeds to step SM7, assuming that “lcnt” is equal to 4. In step SM7, “R” is stored in reference image feature value memory 1024 or in the feature value storage region for partial image “Ri” corresponding to the original image in captured image feature value memory 1025, and the calculation end signal for the partial image feature value is transmitted to control unit 108.
When it is assumed that the values of (lcnt=30 and rcnt=20) are output in step SM2 and (lcnt0=4) is attained, the process proceeds to step SM4. When the conditions of (rcnt>2×lcnt, and rcnt≧rcnt0) are satisfied, the processing in step SM5 is executed. Otherwise, the processing in step SM6 is executed.
In this case, the process proceeds to step SM6, in which “X” is stored in reference image feature value memory 1024 or in the feature value storage region for partial image “Ri” corresponding to the original image in captured image feature value memory 1025, and the calculation end signal for the partial image feature value is transmitted to control unit 108.
Assuming that the values of (lcnt=30, rcnt=70) are output in step SM2 and (lcnt0=4 and rcnt0=4) is attained, the conditions of (lcnt>2×rcnt, and lcnt≧lcnt0) in step SM3 are not satisfied, and the process proceeds to step SM4. When the conditions of (rcnt>2×lcnt, and rcnt≧rcnt0) are satisfied in SM4, the processing in step SM5 is executed. Otherwise, the processing in step SM6 is executed.
In this state, the process proceeds to step SM5. “L” is stored in reference image feature value memory 1024 or in the feature value storage region for partial image “Ri” corresponding to the original image in captured image feature value memory 1025, and the calculation end signal for the partial image feature value is transmitted to control unit 108.
The above calculation of the feature values has the following feature. Reference image “A” or captured image “B” may contain noises. For example, the fingerprint image may be partially lost due to wrinkles in the finger or the like. Thereby, as shown in
As described above, feature value calculate unit 1045 obtains image “WRi” by shifting partial image “Ri” obliquely rightward by a predetermined number of pixel (s), and also obtains image “WLi” by shifting it obliquely leftward by a predetermined number of pixel (s). Further, feature value calculate unit 1045 obtains increase “rcnt” in number of the black pixels that is the difference between partial image “Ri” and image “WRi” obtained by shifting it obliquely rightward by one pixel, and obtains increase “rcnt” in number of the black pixels that is the difference between partial image “Ri” and image “WLi” obtained by shifting it obliquely leftward by one pixel. Based on these increases, feature value calculate unit 1045 determines whether the pattern of partial image “Ri” tends to extend obliquely rightward (e.g., to form a obliquely rightward stripe), to extent obliquely leftward (e.g., to form a obliquely rightward stripe) or to extend in any other direction. Feature value calculate unit 1045 outputs a value (“R”, “L” or “X”) according to the result of this determination.
<Five Kinds of Feature Values>
Feature value calculate unit 1045 may be configured to output all kinds of the feature values already described. In this case, feature value calculate unit 1045 obtains increases “hcnt”, “vcnt”, “rcnt” and “lcnt” of the black pixels according to the foregoing steps. Based on these increases, feature value calculate unit 1045 determines whether the pattern of partial image “Ri” tends to extend horizontally (e.g., lateral stripe), vertically (e.g., longitudinal stripe), obliquely rightward (e.g., obliquely rightward stripe), obliquely leftward (e.g., obliquely leftward stripe) or in any other direction. Feature value calculate unit 1045 outputs a value (“H”, “V”, “R”, “L” or “X”) according to the result of the determination. This output value indicates the feature value of partial image “Ri”.
In this example, “H” and “V” are used in addition to “R”, “L” and “X” as the feature values of partial image “Ri”. Therefore, the feature values of the partial image of the comparison target image can be classified more closely. Therefore, even “X” is issued for a certain partial image when the classification is performed based on the three kinds of feature values, this partial image may be classified to output a value other than “X” when the classification is performed based on the five kinds of feature values. Therefore, the partial image “Ri” to be classified to issue “X” can be detected more precisely.
In this example, the processing in
<Restriction of Search Target>
The target of search by maximum matching score position searching unit 105 can be restricted according to the feature values calculated as described before.
Referring to
Maximum matching score position searching unit 105 searches image “A” in
As can be seen from image (A)-S1, first-found partial image feature value indicates “V”. In image “B”, therefore, the partial image having the feature value of “V” is to be found. In an image (B)-S2-1 illustrated in
In image “B”, the processing is then performed on partial image “g14” (i.e., “V1”) following partial image “g11” and having feature value “V” (image (B)-S1-2 in
Thereafter, the search processing is performed on image “B” in the substantially same manner for partial images having the feature value of “H” and “V” in image “A”, i.e., partial images “g29”, “g30”, “g35”, “g38”, “g42”, “g43”, “g46”, “g47”, “g49”, “g50”, “g55”, “g56”, “g58”-“g62” and “g63” (image (A)-S20 in
Therefore, the number of the partial images searched for in images “A” and “B” by maximum matching score position searching unit 105 is obtained by ((the number of partial images in image “A” having partial image feature value “V”)×(the number of partial images in image “B” having partial image feature value “V”)+(the number of partial images in image “A” having partial image feature value “H”)×(the number of partial images in image “B” having partial image feature value “H”). Referring to
Since the partial image feature value also depends on the pattern represented by the image, description will now be given on images representing patterns different from those in
When images “A” and “C” in
In the above description, the partial images having the same feature value are handled as the search target. However, this is not restrictive, and another manner may be naturally employed, e.g., in view of improvement of the comparison accuracy. For example, the feature values of the partial images of the captured image for the search targets may be determined as follows. In addition to the partial regions of “H”, partial regions of “X” are handled as the search target when the feature value of the reference partial image is “H”. Also, in addition to the partial regions of “V”, partial regions of “X” are handled as the search target when the feature value of the reference partial image is “V”.
When the feature value is “X”, the pattern of the partial image can be determined as neither the longitudinal stripe nor lateral stripe. However, for improving the comparison speed, the partial region exhibiting “X” may be removed from the search range of maximum matching score position searching unit 105.
For improving the accuracy, the values of “L” and “R” may be employed in addition to “H” and “V”.
<Determination of Comparison-Untargeted Image Element>
After the image is subjected to the correction by image correcting unit 104 and the calculation of the feature values of the partial images by feature value calculate unit 1045, it is subjected to processing (step T25b) of determination/calculation for comparison-untargeted image elements.
It is now assumed that each partial image in the image of the comparison target exhibits the feature value of “H”, “V”, “L” or “R” (in the case of the four kinds of values) when it is processed by element determining unit 1047. More specifically, when fingerprint read surface 201 of fingerprint sensor 100 has a stained region or a fingerprint (i.e., finger) is not placed on a certain region, the image cannot be entered through such regions. In this situation, the partial image corresponding to the above region basically takes the feature value of “X”. Using this, element determining unit 1047 detects and determines that the stained partial region in the input image and the partial region unavailable for input of the fingerprint image are the comparison-untargeted image elements, i.e., the image elements other than the comparison target. Element determining unit 1047 assigns the feature value of “E” to the regions thus detected. The fact that the feature value of “E” is assigned to the partial regions (partial image) of the image means that these partial regions (partial images) are excepted from the search range which is searched by maximum matching score position searching unit 105 for the image comparison by comparison determining unit 107, and are excepted from the targets of the similarity score calculation by similarity score calculate unit 106.
Input image “B” in
Element determining unit 1047 reads the feature value calculated by feature value calculate unit 1045 for each of the partial images corresponding to input image “B” in
Element determining unit 1047 searches the feature values of the respective partial images in
More specifically, the feature values of the partial images of input image “B” illustrated in
The above changing or updating will now be described with reference to
In this example, the partial region formed of at least two partial images that have the feature values of “X” and continue to each other in one of the longitudinal, lateral or oblique directions are determined as the comparison-untargeted image elements. However, the conditions of the determination are not restricted to the above. For example, the partial image itself having the feature value of “X” may be determined as the comparison-untargeted image element, and another kind of combination may be employed.
<Similarity Score Calculation and Comparison Determination>
In view of the result of the determination of comparison-untargeted images by element determining unit 1047, the maximum matching score position searching as well as the similarity score calculation based on the result of such determination (step T3 in
When element determining unit 1047 completes the determination, control unit 108 provides the template matching start signal to maximum matching score position searching unit 105, and waits for reception of the template matching end signal.
When maximum matching score position searching unit 105 receives the template matching start signal, it starts the template matching processing in steps S001-S007. In step S001, variable “i” of a count is initialized to “1”. In step S002, the image of the partial region defined as partial image “Ri” in reference image “A” is set as the template to be used for the template matching.
In step S0025, maximum matching score position searching unit 105 searches for reference image feature value memory 1024 to read a feature value “CRi” of partial image “Ri” of the template.
In step S003, the processing is performed to search for the location where image “B” exhibits the highest matching score with respect to the template set in step S002, i.e., the location where the data matching in image “B” occurs with respect to the template to the highest extent. In this searching or determining processing, the following calculation is performed for the partial images of image “B” except for the partial images of the feature values of “E”.
It is assumed that Ri(x, y) represents the pixel density at coordinates (x, y) that are determined based on the upper left corner of rectangular partial image “Ri” used as the template. B(s, t) represents the pixel density at coordinates (s, t) that are determined based on the upper left corner of image “B”, partial image “Ri” has a width of “w” and a height of “h”, and each of the pixels in images “A” and “B” can take the maximum density of “V0”. In this case, matching score Ci(s, t) at coordinates (s, t) in image “B” is calculated based on the density difference of the pixels according to the following equation (1).
In image “B”, coordinates (s, t) are successively updated, and matching score C(s, t) at updated coordinates (s, t) is calculated upon every updating. In this example, the highest score of matching with respect to partial image “Ri” is exhibited at the position in image “B” corresponding to the maximum value among matching scores C(s, t) thus calculated, and the image of the partial image at this position in image “B” is handled as a partial image “Mi”. Matching score C(s, t) corresponding to this position is set as maximum matching score “Cimax”.
In step S004, memory 102 stores maximum matching score “Cimax” at a predetermined address. In step S005, a movement vector “Vi” is calculated according to the following equation (2), and memory 102 stores calculated movement vector “Vi” at a predetermined address.
As described above, image “B” is scanned based on partial image “Ri” corresponding to position “P” in image “A”. When partial region “Mi” in position “M” exhibiting the highest matching score with respect to partial image “Ri” is detected, a directional vector from position “P” to position “M” is referred to as movement vector “Vi”. Since a finger is placed on fingerprint read surface 201 of fingerprint sensor 100 in various manners, one of the images, e.g., image “B” seems to move with respect to the other image “A” (i.e., the reference), and movement vector “Vi” indicates such relative movement. Since movement vector “Vi” indicates the direction and the distance, movement vector “Vi” represents the positional relationship between partial image “Ri” of image “A” and partial image “Mi” of image “B” in a quantified manner.
Vi=(Vix, Viy)=(Mix−Rix, Miy−Riy) (2)
In the equation (2), variables “Rix” and “Riy” indicate the values of x- and y-coordinates of the reference position of partial image “Ri”, and correspond to the coordinates of the upper left corner of partial image “Ri” in image “A”. Variables “Mix” and “Miy” indicate the x- and y-coordinates of the position corresponding to maximum matching score “Cimax” that is calculated from the result of scanning of partial image “Mi”. For example, variables “Mix” and “Miy” correspond to the coordinates of the upper left corner of partial image “Mi” in the position where it matches image “B”.
In step S006, a comparison is made between values of count variable “i” and variable “n”. Based on the result of this comparison, it is determined whether the value of count variable “i” is smaller than the value of variable “n” or not. When the value of variable “i” is smaller than the value of variable “n”, the process proceeds to step S007. Otherwise, the process proceeds to step S008.
In step S007, one is added to the value of variable “i”. Thereafter, steps S002-S007 are repeated to perform the template matching while the value of variable “i” is smaller than the value of variable “n”. This template matching is performed for all partial images “Ri” of image “A”, and the targets of this template matching is restricted on the partial images of image “B” having a feature value “CM” of the same value as corresponding feature value “CRi” that is read by scanning reference image feature value memory 1024 for partial image “Ri” in question. Thereby, maximum matching score “Cimax” of each partial image “Ri” and movement vector “Vi” are calculated.
Maximum matching score position searching unit 105 stores, at the predetermined address in memory 102, maximum matching scores “Cimax” and movement vectors “Vi” that are successively calculated for all partial images “Ri” as described above, and then transmits the template matching end signal to control unit 108 to end the processing.
Then, control unit 108 transmits the similarity score calculation start signal to similarity score calculate unit 106, and waits for reception of the similarity score calculation end signal. Similarity score calculate unit 106 executes the processing in steps S008-S020 in
In step S008, the value of similarity score “P(A, B)” is initialized to 0. Similarity score “P(A, B)” is a variable indicating the similarity score obtained between images “A” and “B”. In step S009, the value of index “i” of movement vector “Vi” used as the reference is initialized to 1. In step S010, similarity score “Pi” relating to movement vector “Vi” used as the reference is initialized to 0. In step S011, index “j” of movement vector “Vj” is initialized to 1. In step S012, a vector difference “dVij” between reference movement vector “Vi” and movement vector “Vj” is calculated according to the following equation (3):
dVij=|Vi−Vj|=sqrt((Vix−Vjx)ˆ2+(Viy−Vjy)ˆ2) (3)
where variables “Vix” and “Viy” represent components in the x- and y-directions of movement vector “Vi”, respectively. Variables “Vjx” and Vjy” represent components in the x- and y-directions of movement vector “Vj”, respectively. A variable “sqrt(X)” represents a square root of “X”, and “Xˆ2” represents an equation for calculating the square of “X”.
In step S013, a value of vector difference “dVij” between movement vectors “Vi” and “Vj” is compared with a threshold indicated by a constant “ε”, and it is determined based on the result of this comparison whether movement vectors “Vi” and “Vj” can be deemed to be substantially the same movement vector or not. When the result of comparison indicates that the value of vector difference “dVij” is smaller than the threshold (vector difference) indicated by constant “ε”, it is determined that movement vectors “Vi” and “Vj” can be deemed to be substantially the same movement vector, and the process proceeds to step S014. When the value is equal to or larger than constant “ε”, it is determined that these vectors cannot be deemed as substantially the same vector, and the process proceeds to step S015. In step S014, the value of similarity score “Pi” is incremented according to the following equations (4)-(6).
Pi=Pi+α (4)
α=1 (5)
α=Cjmax (6)
In equation (4), variable “α” is a value for increasing similarity score “Pi”. When “α” is set to 1 (a=1) as represented by equation (5), similarity score “Pi” represents the number of partial regions that have the same movement vector as reference movement vector “Vi”. When “α” is set to Cjmax (a=Cjmax) as represented by equation (6), similarity score “Pi” represents the total sum of the maximum matching scores obtained in the template matching of partial areas that have the same movement vector as reference movement vector “Vi”. The value of variable “α” may decreased depending on the magnitude of vector difference “dVij”.
In step S015, it is determined whether the value of index “j” is smaller than the value of variable “n” or not. When it is determined that the value of index “j” is smaller than the total number of the partial regions indicated by variable “n”, the process proceeds to step S016. Otherwise, the process proceeds to step S017. In step S016, the value of index “j” is incremented by one. Through the processing in steps S010-S016, similarity score “Pi” is calculated using the information about the partial regions that are determined to have the same movement vector as movement vector “Vi” used as the reference. In step S017, movement vector “Vi” is used as the reference, and the value of similarity score “Pi” is compared with that of variable “P(A, B)”. When the value of similarity score “Pi” is larger than the maximum similarity score (value of variable “P(A, B)”) already obtained, the process proceeds to step S018. Otherwise, the process proceeds to step S019.
In step S018, variable “P(A, B)” is set to a value of similarity score “Pi” with respect to movement vector “Vi” used as the reference. In steps S017 and S018, when similarity score “Pi” obtained using movement vector “Vi” as the reference is larger than the maximum value (value of variable “P(A, B)”) of the similarity score among those already calculated using other movement vectors as the reference, movement vector “Vi” currently used as the reference is deemed as the most appropriate reference among indexes “i” already obtained.
In step S019, the value of index “i” of reference movement vector “Vi” is compared with the number (value of variable “n”) of the partial regions. When the value of index “i” is smaller than the number of the partial areas, the process proceeds to step S020, in which index “i” is incremented by one.
Through steps S008 to S0202, the score of similarity between images “A” and “B” is calculated as the value of variable “P(A, B)”. Similarity score calculate unit 106 stores the value of variable “P(A, B)” thus calculated at the predetermined address in memory 102, and transmits the similarity score calculation end signal to control unit 108 to end the processing.
Subsequently, control unit 108 transmits the comparison determination start signal to comparison determining unit 107, and waits for reception of the comparison determination end signal. Comparison determining unit 107 performs the comparison and determination (step T4). More specifically, it compares the similarity score indicated by the value of variable “P(A, B)” stored in memory 102 with a predetermined comparison threshold “T”. When the result of comparison indicates (variable P(A, B)≧T), comparison determining unit 107 determines that images “A” and “B” are obtained from the same fingerprint, and writes a value, e.g., of “1” indicating the matching as the comparison result at a predetermined address in memory 102. Otherwise, comparison determining unit 107 determines that images “A” and “B” are obtained from different fingerprints, respectively, and writes a value, e.g., of “0” indicating the mismatching as the comparison result at the predetermined address in calculation memory 1022. Thereafter, it transmits the comparison determination end signal to control unit 108 to end the processing.
Finally, control unit 108 outputs the comparison results stored in memory 102 via display 610 or printer 690 (step T4), and ends the image comparison.
In this embodiment, both images “A” and “B” are input through image input unit 101. However, the following configuration may be employed. Memory 102 includes a registered image storage for registering in advance a plurality of partial images “Ri” of image “A”, and comparison processing unit 11 reads partial image “Ri” of image “A” from the registered image storage. Image “B” is inputted through image input unit 101.
In this embodiment, all or a part of image correcting unit 104, feature value calculate unit 1045, element determining unit 1047, maximum matching score position searching unit 105, similarity score calculate unit 106, comparison determining unit 107 and control unit 108 may be implemented by a ROM such as memory 624 storing programs of the processing procedures and a processor such as CPU 622 for executing the programs.
A specific example of the comparison processing according to the embodiment as well as the effect thereof are as follows.
When input image “B” is a fingerprint image stained as indicated by a hatched circle in
Assuming that the partial images of input image “B” must have the matching rate of 90% (0.9) or more with respect to reference image “A”, the comparison result would be “mismatching” if image “B” having the feature values in
However, element determining unit 1047 in the embodiment designates the stained potential regions as the comparison-untargeted image elements. Therefore, the comparison between images “B” and “A” is performed by comparing the comparison image in
As described above, the embodiment can eliminate the processing of checking the presence of smear on fingerprint read surface 201 of fingerprint sensor 100, and thus can eliminate the processing that is required before the fingerprint comparing processing in the prior art. Further, the smear is not detected directly from the image read through the whole fingerprint read surface 201, but is detected according to the information obtained in connection with the partial images determined to have the feature values of “X”. Therefore, the reference for calculating the feature value of “X” by feature value calculate unit 1045 can be variable according to the required rate of matching, and thereby the comparison processing can be continued without requiring cleaning by changing the reference when the position/size of the smear is practically ignorable. Consequently, the more images can be processed per unit time. Also, such a situation can be suppressed that the user is requested to input the fingerprint again due to the smear and/or to clean out the smear, and thus the inconvenience to the user can be prevented.
The processing function for image comparison already described is achieved by programs. According to a second embodiment, such programs are stored on computer-readable recording medium.
In the second embodiment, the recording medium may be a memory required for processing by the computer show in
The above recording medium can be separated from the computer body. A medium stationarily bearing the program may be used as such recording medium. More specifically, it is possible to employ tape mediums such as a magnetic tape and a cassette tape as well as disk mediums including magnetic disks such as FD 632 and fixed disk 626, and optical disks such as CD-ROM 642, MO (Magnetic Optical) disk, MD (Mini Disk) and DVD (Digital Versatile Disk), card mediums such as an IC card (including a memory card) and optical card, and semiconductor memories such as a mask ROM, EPROM (Erasable and Programmable ROM), EEPROM (Electrically EPROM) and flash ROM.
Since the computer in
The contents stored on the recording medium are not restricted to the program, and may be data.
Although the present invention has been described and illustrated in detail, it is clearly understood that the same is by way of illustration and example only and is not to be taken by way of limitation, the spirit and scope of the present invention being limited only by the terms of the appended claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2006-153826 | Jun 2006 | JP | national |
