Method and system for fast and accurate face detection and face detection training

Abstract

A face detection method where a cascaded weak classifier and a result of a previous stage are combined. The weak classifier is based on a modified double sigmoid function to precisely and effectively estimate each Haar feature. The face detection method includes a method of training a parameter of a new weak classifier.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects of the present invention will become apparent and more readily appreciated from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings of which:

FIG. 1 is a diagram illustrating an example of a face detection method according to the related art;

FIG. 2 is a diagram illustrating an example of a cascaded face candidate verifier structure according to the related art;

FIG. 3 is a diagram illustrating an example of a binary weak classifier and a discrete strong classifier according to the related art;

FIG. 4 is a diagram illustrating a histogram with respect to a Haar feature value;

FIG. 5 is a diagram illustrating a structure of a fast and accurate face detection system according to an exemplary embodiment of the present invention;

FIG. 6 is a diagram illustrating a double sigmoid function according to an exemplary embodiment of the present invention;

FIG. 7 is a diagram illustrating a parameter of a modified double sigmoid weak classifier according to an exemplary embodiment of the present invention;

FIG. 8 is a diagram illustrating a combination structure of a face candidate verifier according to an exemplary embodiment of the present invention;

FIG. 9 is a flowchart illustrating operations of training a face detection system according to another exemplary embodiment of the present invention; and

FIG. 10 is a diagram illustrating a size of coefficient c which varies according to a stage.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Reference will now be made in detail to exemplary, non-limiting embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The embodiments are described below in order to provide a description with respect to the figures.

FIG. 5 is a diagram illustrating a structure of a fast and accurate face detection system according to an exemplary embodiment.

A face detection system 500 includes a weak classifier calculation unit 510 calculating a weak classifier associated with a stage, from a modified double sigmoid function. Specifically, the weak classifier calculation unit 510 modifies a typical double sigmoid function, and provides the modified double sigmoid function. Also, the weak classifier calculation unit 510 uses the induced modified double sigmoid function, and calculates the weak classifier. The weak classifier may effectively estimate a Haar feature with respect to a sub-window.

FIG. 6 is a diagram illustrating a double sigmoid function according to an exemplary embodiment of the present invention. Part i) of FIG. 6 illustrates a typical double sigmoid function. Part ii) of FIG. 6 illustrates the modified double sigmoid function.

The typical double sigmoid function of FIG. 6 (i) determines a range of a weak classifier f(g) with respect to a Haar feature value g as a continuous value from 0 to 1. In this instance, the Haar feature value g is in an unbounded range. In this instance, a configuration of the double sigmoid function to be applied may vary according to whether the Haar feature value g is greater than a set threshold t.

Part i) of FIG. 6 illustrates that, when the Haar feature value g is less than the set threshold, a value of the weak classifier f(g) is calculated to be less than a value, for example, 0.5. In this instance, the value of the weak classifier f(g) is calculated by the double sigmoid function.

The typical double sigmoid function may be represented as a function in which the Haar feature value g is a parameter, as shown in equation 1.

$\begin{array}{cc}f\left(g\right)=\{\begin{array}{cc}\frac{1}{1+\exp \left(-2\frac{g-t}{r1}\right)}& \mathrm{if}g<t\\ \frac{1}{1+\exp \left(-2\frac{g-t}{r2}\right)}& o.w.\end{array}& \left[\mathrm{Equation}1\right]\end{array}$

Here, the t is a threshold of two sigmoids, the r₁ determines a variation of a first sigmoid, and the r₂ determines a variation of a second sigmoid.

In part ii) of FIG. 6, a modification of the function is performed so that the typical double sigmoid function f(g) varies with a feature histogram. Specifically, the modified double sigmoid function of part ii) of FIG. 6 may determine a range of a weak classifier f(g) with respect to a Haar feature value g as a continuous value from −b to a. In this instance, the Haar feature value g is in unbounded range.

The modified double sigmoid function may be represented as a function in which the Haar feature value g is an input parameter, as shown in equation 2.

Equation 2

$f\left(g\right)=[\begin{array}{cc}b\frac{1-\exp \left(-2\frac{g-t}{r_1}\right)}{1+\exp \left(-2\frac{g-t}{r_1}\right)}& \mathrm{if}g<t\\ a\frac{1-\exp \left(-2\frac{g-t}{r_2}\right)}{1+\exp \left(-2\frac{g-t}{r_2}\right)}& \mathrm{Otherwise}\end{array}$

Here, b is a weight of a first sigmoid, and a is a weight of a second sigmoid.

Specifically, a weak classifier calculation unit 510 of the face detection system 500 may calculate the weak classifier f(g) by using the modified double sigmoid function.

The modified double sigmoid function f(g) of equation 2 has the following features:

• • 1) The modified double sigmoid function is a continuous function, not a discrete function.
  • 2) The modified double sigmoid function significantly varies near the threshold, and varies substantially less far from the threshold.
  • 3) The modified double sigmoid function changes a variation of confidence according to a distribution of a positive sample and a negative sample.
  • 4) The modified double sigmoid function has limits on the two ends.
  • 5) The modified double sigmoid function may use a lookup table for fast computation.

In an exemplary embodiment, only the five parameters, t, r₁, r₂, b, and a, need to be estimated with respect to calculation of each of the weak classifier f(g).

FIG. 7 is a diagram illustrating a parameter of a modified double sigmoid weak classifier according to an exemplary embodiment.

As described above, a weak classifier of a stage is calculated by a modified double sigmoid function. Also, t, r₁, r₂, b, and a described above are used as a parameter in the modified double sigmoid function. The parameter may be estimated by distributions of a negative sample and a positive sample of the modified double sigmoid function.

FIG. 7 illustrates a feature distribution in which a first sigmoid having a variation r₁ and a second sigmoid having a variation r₂ are overlapped at the location of a threshold t.

The parameter t indicates a threshold which can most suitably distinguish between the negative sample and the positive sample in the feature distribution as shown in FIG. 7. The parameter t may be a set value. In this instance, the set value ensures a sum of a negative sample range and a positive sample range is a minimum. In this instance, the negative sample range is greater than the set value, and the positive sample range is less than the set value.

Specifically, the parameter t is determined by,

$\begin{array}{cc}t=\text{arg}\min \left(\sum _{X_i\in S_N\&g_i>t}W_ig_i+\sum _{X_i\in S_P\&g_i<t}W_ig_i\right)& \left[\mathrm{Equation}3\right]\end{array}$

Here, S_p is a set of positive training samples, and S_n is a set of negative training samples. Also, g_i is a feature value of an i^th sample x_i with respect to a feature g, and the w_i is a histogram value of the feature value g_i.

An antecedent of equation 3 may indicate an integral value of the negative sample range which overlaps the positive sample range. In this instance, the negative sample range which overlaps the positive sample range is included in the negative sample range, and a range in which the feature g is greater than the set value t.

A result of equation 3 may indicate an integral value of the positive sample range which overlaps the negative sample range. In this instance, the positive sample range which overlaps the negative sample range is included in the positive sample range, and a range in which the feature g is less than the set value t.

Specifically, equation 3 determines the predetermined set value t as an optimal parameter t of the threshold. In this instance, the set value t is under a condition that a range where the negative sample range and the positive sample range are overlapped is a minimum.

Parameter r₁ determines a set value r₁. In this instance, the set value r₁ calculates a constant value, when dividing the negative sample range, which is less than the predetermined set value r₁ based on a parameter t, into a sample set S_N.

Also, a parameter r₂ determines a set value r₂. In this instance, the set value r₂ calculates a constant value, when dividing the positive sample range, which is greater than the set value r₂ based on the parameter t, into a sample set S_P.

In this instance, the constant values associated with the parameter r₁ and the parameter r₂ are identical.

Specifically, the parameter r₁ and the parameter r₂ is determined by,

$\begin{array}{cc}\frac{\sum _{X_i\in S_N\&g_i<t-r_1}W_ig_i}{S_N}=\frac{\sum _{X_i\in S_P\&g_i>t+r_2}W_ig_i}{S_P}=\mathrm{CONST}& \left[\mathrm{Equation}4\right]\end{array}$

Parameter a is determined by dividing the integral value of the positive sample range into a sum of the integral value of the positive sample range and the integral value of the negative sample range. In this instance, the integral value of the negative sample range and the integral value of the positive sample range are greater than the parameter t.

Specifically, the parameter a is determined by,

Equation 5

$a=\frac{\sum _{X_i\in S_P\&g_i>t}W_ig_i}{\sum _{X_i\in S_N\&g_i>1}W_ig_i+\sum _{X_i\in S_P\&g_i>t}W_ig_i}$

Here, the integral value,

$\sum _{X_i\in S_N\&g_i>t}W_ig_i,$

of the negative sample range which is greater than the parameter t indicates an error.

Parameter b is determined by dividing the integral value of the negative sample range into a sum of the integral value of the negative sample range and the integral value of the positive sample range. In this instance, the integral value of the negative sample range and the integral value of the positive sample range are less than the parameter t, respectively.

Specifically, the parameter b is determined by,

$\begin{array}{cc}b=\frac{\sum _{X_i\in S_N\&g_i<t}W_ig_i}{\sum _{X_i\in S_N\&g_i<t}W_ig_i+\sum _{X_i\in S_P\&g_i<t}W_ig_i}& \left[\mathrm{Equation}6\right]\end{array}$

Here, the integral value of the positive sample range,

$\sum _{X_i\in S_P\&g_i<t}W_ig_i,$

which is less than the parameter t indicates an error.

Accordingly, the face detection system 500 according to an exemplary embodiment may determine five parameters associated with a calculation of the modified double sigmoid weak classifier.

Referring again to FIG. 5, the face detection system 500 includes a Haar feature estimation unit 520 estimating a Haar feature by using the calculated weak classifier. Specifically, the Haar feature estimation unit 520 compares a calculated value of the modified double sigmoid function with a reference value. According to a result of the comparison, samples associated with the weak classifier may be distinguished as the negative sample or the positive sample.

As an example, when the reference value is set as 0, the face detection system 500 compares the calculated value and 0. According to the result of the comparison, the calculated value, a weak classifier f(g), is less than 0, which is negative, the face detection system 500 determines the sample of the associated weak classifier as a negative sample. Also, when the weak classifier f(g) is greater than 0, which is positive, the face detection system 500 distinguishes the sample of the associated weak classifier as a positive sample.

Also, the face detection system 500 includes a comparison unit 530 comparing a calculated value of a strong classifier H_n(x), based on an estimation of the Haar function, with a reference value. Specifically, the comparison unit 530 calculates a calculated value of the strong classifier H_n(x) which is a weighted sum of the weak classifier with respect to a particular stage. Then, the comparison unit 530 compares the calculated value of the strong classifier H_n(x) with a reference value which has been set by an operator, for example, an operator of the system.

Also, the face detection system 500 includes a determination unit 540 determining a sub-window of an input image associated with the stage as a face or a non-face, based on the result of the comparison by the comparison unit 530. Specifically, when the calculated strong classifier H_n(x) is greater than the reference value, the determination unit 540 determines and confirms the non-face with respect to the sub-window in a current stage.

When the calculated strong classifier H_n(x) does not satisfy the reference value, the face detection system 500 calculates a weighted sum associated with a subsequent weak classifier, and compares the reference value with the strong classifier H_n(x) including the weighted sum. Otherwise, the face detection system 500 moves to a subsequent stage and determines the sub-window as a face or a non-face again.

Accordingly, the face detection system 500 proposes a weak classifier by using the modified double sigmoid function. Accordingly, the face detection system 500 may estimate the Haar feature with a high accuracy and use less weak classifiers for each stage.

The face detection system 500 combines a cascaded and a result of a previous stage. Accordingly, the face detection system 500 may improve an accuracy of face detection, and reduce computation time.

FIG. 8 is a diagram illustrating a combination structure of a face candidate verifier according to an exemplary embodiment of the present invention. The face detection system 500 uses a confidence of a previous stage, an n-1^st stage, when extracting a confidence of a current stage. Accordingly, the face detection system 500 may reduce a number of weak classifiers to be calculated, and also computation time.

Specifically, according to the related art, the face detection method independently estimates a sub-window for each stage regardless of relationship of stages. However, the face detection system 500 uses the confidence of the previous stage in the current stage. Particularly, the face detection system 500 uses the confidence of the previous stage as a first weak classifier of the current stage, and a performance of the previous stage as a weight.

A strong classifier H_n(x) is determined by,

H _n(x)=β_n-1H_n-1(x)+Σα_ih_i(x)   [Equation 7]

The H_n-1(x) is a strong classifier of an n-1^th stage and the β_n-1 indicates a weight of the H_n-1(x). Also, equation 7 is a combination of a cascaded and a weighed chain. Through equation 7, the sub-window is estimated.

As shown in equation 7, the confidence of the previous stage, H_n-1(x), is used as a first weak classifier of the current stage. Accordingly, a confidence which has been extracted from a first stage has the greatest affect on an estimation of all sub-windows.

Accordingly, the face detection system 500 may require more precise confidence extraction with respect to the first stage or first several stages, when determining a particular sub-window.

Hereinafter, according to another exemplary embodiment, a method and system for training the face detection system 500 will be described.

FIG. 9 is a flowchart illustrating operations of training a face detection system according to another exemplary embodiment. The face detection system 500 prepares a number of stages for estimating a particular sub-window, and estimates a non-face determination or a non-face determination deferral with respect to the sub-window for each stage.

Specifically, when a calculated value of a strong classifier satisfies a value in a particular stage, the face detection system 500 determines and confirms the associated sub-window as a non-face.

Also, when the calculated value of the strong classifier does not satisfy the value in the particular stage, the face detection system 500 calculates a subsequent weak classifier, and determines the non-face determination or the non-face determination deferral in a current stage again. Otherwise, the face detection system 500 continues to a subsequent stage, and repeatedly estimates the sub-window.

When the non-face is not determined in the prepared stages, the face detection system 500 may determine the associated sub-window as the face, and recognizes that the sub-window is associated with a human face.

The face detection system 500 may determine weak classifiers of each of the stages and each of the weights through training by a face detection training method or a face detection training system.

In FIG. 9, the face detection training method is illustrated. In this instance, the face detection training method determines a weak classifier and weight of a stage by using a training sample, and creates a strong classifier. The face detection training method according to another exemplary embodiment may be performed by the face detection training system.

In operation S910 after start S900, the face detection training system initializes a training sample weight. In this instance, a value of the training sample weight is set as a size. Also, the training sample weight in each stage is controlled by using a coefficient c. A positive sample weight may be defined as (c−1)/(2*Np*(c+1)). Also, a negative sample weight may be defined as 1/(Nn*(c+1)). In this instance, the Np indicates a positive sample number, and the Nn indicates a negative sample number.

FIG. 10 is a diagram illustrating a size of coefficient c which may vary according to a stage. Also, the coefficient c is large in the initial several stages, and the coefficient c is notably decreased in later stages. For example, the coefficient c of a first stage is 100, whereas the coefficient c of a twentieth stage may be approximately 1. Accordingly, the positive sample is considered more important than the negative sample, in the initial several stages in which the coefficient c is large.

In operation S920, the face detection training system selects the best modified double sigmoid weak classifier. In this instance, an i^th weak classifier of an n^th stage is sequentially found. As an example, the face detection training system may calculate a first weak classifier h₁ of the first stage by the modified double sigmoid function. In this instance, parameters t, a, b, r₁, and r₂, which are used in the modified double sigmoid function, may be determined by using equations 3, 4, 5, and 6, respectively. In this instance, a condition that the training sample weight w is set as an initial value is required.

In operation S930, the face detection training system estimates a weight of the weak classifier from the calculated weak classifier h₁. In this instance, an i^th weight which is multiplied by the i^th weak classifier is calculated. Accordingly, the face detection training system allows the i^th weight of the strong classifier to be calculated.

When estimating a weight of the weak classifier, the face detection training system uses a cost function, and calculates a weight α in which a calculated value of the cost function is a minimum.

The cost function may be determined by,

$\begin{array}{cc}{Z}_t=\sum w_t\left(\right)·\exp \left(-{\alpha }_ty_ih_t\left(x_i\right)\right)·\exp \left({\mathrm{\lambda \alpha }}_ty_i\right)\lambda =\frac{c-1}{c+1}& \left[\mathrm{Equation}8\right]\end{array}$

Here, a training sample is defined as a combination of an input area and a class index, and indicated as (x₁, y₁), (x₂, y₂) . . . , and (x_m, y_m). The w_t(i) is a training sample weight, and the α_t is a weak classifier weight. The w_i(i) indicates an initial value.

When calculating the weight α of the first weak classifier h₁, the face detection training system may calculate a weight α₁ by using the minimum value of the cost function. In this instance, the minimum value of the cost function is a value of the cost function which can achieve the best classification performance by using the Newton algorithm.

Accordingly, the face detection training system may calculate the first weak classifier h₁ and the first weight α₁ with respect to a first stage.

In operation S940, the face detection training system updates the sample weight. Specifically, in operation S940, the face detection training system updates a training sample weight by using equation 9 below.

w _t+1(i)=w_t(i)·exp(−α_ty_ih_t(x_i))·exp(λα_ty_i)

In operation S950, the face detection training system determines whether a strong classifier satisfies a set performance. In this instance, the strong classifier includes a value which is acquired by multiplying the calculated first weak classifier h₁ and the first weight α₁.

Specifically, the face detection training system multiplies the first weak classifier h₁ and the first weight α₁, composes the strong classifier H_n(x), and determines whether a classification performance of the strong classifier H_n(x) satisfies the set performance.

As a result of the determination, when the classification performance satisfies the set performance, i.e., the “Y” branch of operation S950, the face detection training system stops training in operation S960. In this instance, the strong classifier H_n-1(x) becomes β_n-1*H_n-1+h₁*α₁.

As a result of the determination, when the classification performance does not satisfy the set performance, i.e., the “N” branch of operation S950, the face detection training system increases a number of the weak classifier t, and calculates a subsequent weak classifier h₂ and the weight α₂. Then, the face detection training system estimates the training samples by using an i+1^th weight and the strong classifier H_n(x) including the i+1^th weight again. In this instance, the i+1^th weight is acquired by multiplying the calculated weak classifier h₂ and the weight α₂. Specifically, the face detection training system performs the operations S920, S930, S940, and S950 again. When the strong classifier H_n(x) satisfies the set performance, the strong classifier H_n(x) is calculated as β_n-1*H_n-1+(h₁*α₁+h₂*α₂). More specifically, when the strong classifier H_n(x) does not satisfy the set performance, the face detection training system increases the number of weak classifier t, and loops.

It can be understood that each of blocks of the flowchart and combination of the flowchart of FIG. 9, as well as the above-disclosed contents, can be executed by using computer program instructions. Since the computer program instructions can be included in a processor of a general computer, a special-purpose computer, or a programmable data processing device, the instructions executed by the processors of the computer or another programmable data processing device may create a unit that executes functions described in the block of the flowchart.

These computer program instructions can be stored in a computer usable memory or computer readable memory that can aim at a computer or another programmable data processing device so as to implement the computer program instruction in a specific manner. The instructions stored in the computer usable memory or the computer readable memory can produce manufacture items including the instruction units that execute the functions described in the blocks in the block of the flowchart. Since the computer program instructions can be included in a computer or another programmable data processing device, the instructions that create a process in which a series of operation stages are performed on the computer or another programmable data processing device and executed by a computer and causes the computer or another programmable data processing device to be performed can supply procedures so as to execute the functions described in the blocks of the flowchart.

Further, each block can represent a module, a segment, or a part of codes that includes one and more executable instructions for executing specific logic functions. In addition, in some modified embodiments, it should be understood that the function described in the blocks can be executed in disorder. For example, adjacent two blocks can be substantially performed at the same time or can be performed in reverse order in accordance with a function corresponding to the block.

Although exemplary embodiments have been shown and described, the present invention is not limited thereto. Instead, it will be appreciated by those skilled in the art that changes may be made to these exemplary, non-limiting embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

According to the present invention, a fast and accurate face detection method and system may provide a weak classifier by using a modified double sigmoid function, and thereby may estimate a Haar feature with a high accuracy and use fewer weak classifiers for each stage, but need not do so in order to be within the scope of the invention.

Also, according to the present invention, a fast and accurate face detection training method and system may provide a combination of a cascaded and a weighted chain, and thereby may use a confidence of a previous stage, but need not do so in order to be within the scope of the invention.

Also, according to the present invention, a fast and accurate face detection training method and system may provide a method of training weight of a strong classifier, and thereby may obtain an optimal weight and a parameter in each weak classifier, but need not do so in order to be within the scope of the invention.

Also, according to the present invention, a fast and accurate face detection system may be embodied.

Compared with a related art algorithm, substantially fewer weak classifiers may be required for face candidate verification at each stage in the algorithm of the exemplary embodiment.

	TABLE 1
	Stage No.
	1	2	3	4	1~20
	Conventional algorithm	5	6	7	13	2018
	Exemplary embodiment	3	4	7	8	928

In the table 1, a number of required weak classifiers is 928 in the algorithm according to the exemplary embodiment, whereas the number of required weak classifiers is 2,018 in the related art algorithm.

Also, according to the present invention, computation complexity may be reduced so that the present invention is 1.4 times faster than a related art face detection method, and 3.1 times faster than an Intel OpenCV model, but need not do so in order to be within the scope of the invention.

Also, according to the present invention, a detail detection rate may be substantially improved, but need not do so in order to be within the scope of the invention.

TABLE 2
		Conventional	Exemplary
Test DB	Image number	algorithm	embodiment
FRGC Exp4 DB	8014	97.04%	99.89%
All Test DB	20558	92.94%	95.56%

In the table 2, it is shown that the detail detection rate according to the exemplary embodiment in the ‘FRGC Exp4 DB’ and ‘All Test DB’ may be improved, but need not do so in order to be within the scope of the invention.

Also, slight posing or some occlusion may not affect the face detection system, but need not do so in order to be within the scope of the invention.

Additionally, computation time may be reduced, but need not do so in order to be within the scope of the invention. For example, the computation time with respect to an image hose size is 400*390, is about 101.2 ms, the computation time with respect to image whose size is 600*450, is about 201 ms, and the computation time with respect to image whose size is 320*240, is about 32 ms.

Claims

1. A face detection method comprising: calculating a weak classifier associated with a stage, from a modified double sigmoid function; andestimating a Haar feature using the calculated weak classifier.

2. The method of claim 1, wherein the stage comprises a single strong classifier H(x), and wherein a strong classifier Hn(x) of an nth stage is given by, Hn(x)=βn-1Hn-1(x)+Σαihi(x)which is acquired by adding a value acquired by multiplying a strong classifier Hn-1(x) of an n+1th stage and a weight βn-1, and a weighted sum of up to an ith weak classifier of the nth stage.

3. The method of claim 2, wherein the βn-1*Hn-1(x) is a first weak classifier of the Nth stage.

4. The method of claim 2, further comprising: comparing a calculated value of the strong classifier Hn(x), based on an estimation of the Haar feature, with a reference value; anddetermining a sub-window of an input image associated with the stage as one of a face and a non-face, according to a result of the comparing.

5. The method of claim 1, wherein the modified double sigmoid function is given by,

6. The method of claim 5, wherein the t is given by,

7. The method of claim 5, wherein r1 and r2 are given by,

8. The method of claim 5, wherein a and b are respectively given by,

9. The method of claim 5, wherein the estimating estimates a sample associated with the weak classifier as a positive sample if a calculated value of the modified double sigmoid function f(g) is greater than a reference value, and the estimating estimates the sample associated with the weak classifier as a negative sample if the calculated value of the modified double sigmoid function f(g) is less than the reference value.

10. A face detection training method comprising: calculating a modified double sigmoid function-based tth weak classifier considering a training sample weight;calculating a weight of the calculated tth weak classifier;updating the training sample weight; andestimating whether a strong classifier, which is a weighted sum of up to a tth weak classifier, satisfies a standard.

11. The method of claim 10, wherein the estimating comprises: performing the calculating of the tth weak classifier, the calculating of the weight, and the updating with respect to a t+1th weak classifier if the strong classifier Hn(x) does not satisfy the standard; andestimating whether a strong classifier, which is a weighted sum of up to a t+1th weak classifier, satisfies the standard.

12. The method of claim 10, wherein the estimating comprises terminating the training if the strong classifier Hn(x) satisfies the standard.

13. A computer-readable recording medium configured to store instructions thereon for implementing a face detection method comprising: calculating a weak classifier associated with a stage, from a modified double sigmoid function; andestimating a Haar feature by using the calculated weak classifier.

14. The computer readable medium of claim 13, wherein the stage comprises a single strong classifier H(x), and wherein a strong classifier Hn(x) of an nth stage is given by, Hn(x)=βn-1Hn-1(x)+Σαihi(x)

15. A computer-readable recording medium configured to store instructions for implementing a face detection training method comprising: calculating a modified double sigmoid function-based tth weak classifier considering a training sample weight;calculating a weight of the calculated tth weak classifier;updating the training sample weight; andestimating whether a strong classifier, which is a weighted sum of up to a tth weak classifier, satisfies a standard.

16. The computer readable medium of claim 15, wherein the estimating comprises: performing the calculating of the tth weak classifier, the calculating of the weight, and the updating with respect to a t+1th weak classifier if the strong classifier Hn(x) does not satisfy the standard; andestimating whether a strong classifier, which is a weighted sum of up to a t+1th weak classifier, satisfies the standard.

17. A face detection system comprising: a weak classifier calculation unit that calculates a weak classifier associated with a stage, from a modified double sigmoid function;a Haar feature estimation unit that estimates a Haar feature using the calculated weak classifier;a comparison unit that compares a calculated value of a strong classifier Hn(x), based on an estimation of the Haar feature, with a reference value; anda determination unit that determines a sub-window of an input image associated with the stage as a face or a non-face, based on a result of the comparison by the comparison unit.

18. The system of claim 17, wherein the stage comprises a single strong classifier H(x), and wherein a strong classifier Hn(x) of an nth stage is given by, Hn(x)=βn-1Hn-1(x)+Σαihi(x)which is acquired by adding a value acquired by multiplying a strong classifier Hn-1(x) of an n-1th stage and a weight βn-1, and a weighted sum of up to an ith weak classifier of the nth stage.

19. The system of claim 17, wherein the modified double sigmoid function is given by,