BRIEF DESCRIPTION OF THE DRAWINGS
The present invention will become more fully understood from the detailed description and the accompanying drawings, wherein:
FIG. 1 illustrates fingerprint features at Level 1 (upper row), Level 2 (middle row) and Level 3 (lower row), in accordance with the prior art;
FIGS. 2
a and 2b illustrate the role of pores in fragmentary latent print examination, wherein FIGS. 2a and 2b are fingerprint segments from different fingers, wherein each figure shows a bifurcation at the same location on similar patterns such that normal examination would find them in agreement, but their relative pore locations differ, in accordance with the prior art;
FIG. 3 illustrates characteristic features of friction ridges, in accordance with the prior art;
FIG. 4
a illustrates friction ridge skin including a three-dimensional representation of the structure of ridged skin, wherein the epidermis is partly lifted from the dermis to expose the dermal papillae, in accordance with the prior art;
FIG. 4
b illustrates friction ridge skin including a finger seen during the maceration process shows (A) the regular linear disposition of vessels along the fingerprints and (B) two rows of vessels are seen at low magnification revealing perfect correspondence, in accordance with the prior art;
FIG. 5 illustrates open and closed pores in a 1000 ppi live-scan fingerprint image obtained using a CrossMatch 1000ID scanner, in accordance with the prior art;
FIGS. 6
a-6c illustrate fingerprint image resolution, wherein the same fingerprint is captured at three different image resolutions including 380 ppi with an Identix 200DFR (see FIG. 6a), 500 ppi with a CrossMatch 1000ID (see FIG. 6b), and 1000 ppi with a CrossMatch 1000ID (see FIG. 6c), in accordance with the prior art;
FIGS. 7
a and 7b illustrate pore detection based on skeletonization, wherein FIG. 7a shows a fingerprint image (2000 ppi) with detected pores (in the square box) and FIG. 7b shows the raw skeleton image where end points and branch points are tracked for pore extraction, in accordance with the prior art;
FIGS. 8
a and 8b illustrate pore detection in fingerprint fragments, wherein FIG. 8a shows detection of open pores and FIG. 8b shows extraction of open pores (in white) and closed pores (in black), in accordance with the prior art;
FIGS. 9
a-9c illustrate the sensitivity of skeletonization to various skin conditions and noise, wherein effects of degradation on gray scale (see FIG. 9a), binary (see FIG. 9b), and raw skeleton images (see FIG. 9c) are observed for three different sources of noise (e.g., wet finger, dry finger, and wrinkle), in accordance with the prior art;
FIG. 10 illustrates impressions of the same finger at 1000 ppi, wherein it is observed that ridge contours are more reliable Level 3 features compared to pores, in accordance with the general teachings of the present invention;
FIGS. 11
a-11f illustrate pore extraction, including a partial fingerprint image at 1000 ppi (see FIG. 11a), enhancement of ridges in the image shown in FIG. 11a using Gabor filters (see FIG. 11b), a linear combination of FIGS. 11a and 11b (see FIG. 11c), a wavelet response (s=1.32, e.g., see equation (3)) of the image in FIG. 11a (see FIG. 11d), a linear combination of FIGS. 11b and 11d (see FIG. 11e), and extracted pores (black circles) after thresholding the image in FIG. 11e (see FIG. 11f), in accordance with the general teachings of the present invention;
FIGS. 12
a-12c illustrate ridge contour extraction, including wavelet response (s=1.74, e.g., see equation (3)) of the image in FIG. 11a (see FIG. 12a), ridge contour enhancement using a linear subtraction of wavelet response in FIG. 12a and Gabor enhanced image in FIG. 11a (see FIG. 12b), and extracted ridge contours after binarizing FIG. 12b and convolving with filter H (see FIG. 12c), in accordance with the general teachings of the present invention;
FIG. 13 illustrates a system flow chart, wherein fingerprint features at three different levels are utilized in a hierarchical fashion, in accordance with the general teachings of the present invention;
FIGS. 14
a-14c illustrates different levels of fingerprint features detected in FIG. 6c, wherein these features are utilized in the matching system of the present invention including orientation field (Level 1) (see FIG. 14a), minutiae points (Level 2) (see FIG. 14b), and pores and ridge contours (Level 3) (see FIG. 14c), in accordance with the general teachings of the present invention;
FIG. 15 illustrates the effect of using Level 3 features, wherein the overlap region of the genuine and imposter distributions of matched minutiae is reduced after Level 3 features are utilized, wherein curves corresponding to MP are based on Level 2 features alone and curves corresponding to MP′ are based on Level 2 and Level 3 features, in accordance with the general teachings of the present invention;
FIGS. 16
a and 16b illustrate an example of using an ICP algorithm for Level 3 matching, wherein after k=6 iterations, the match distance between PT and PQ was reduced from 3.03 in FIG. 16a to 1.18 in FIG. 16b, in accordance with the general teachings of the present invention;
FIG. 17 illustrates ROC (i.e., receiver operating characteristic) curves for the Level 2 matcher (minutiae-based) and the matcher of the present invention that utilizes Level 2 and Level 3 features, in accordance with the general teachings of the present invention; and
FIG. 18 illustrates ROC curves for high quality (HQ) and low quality (LQ) images for the Level 2 matcher (minutiae-based) and the matcher of the present invention, in accordance with the general teachings of the present invention.
Patent Application
20070230754
