Face detector training method, face detection method, and apparatuses

• US 9,836,640 B2
• Filed: 02/16/2015
• Issued: 12/05/2017
• Est. Priority Date: 02/17/2014
• Status: Active Grant

• Current Assignee: Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
• Original Assignee: Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
• Inventors: Zhang, Yu, Chai, Zhenhua, Du, Zhijun
• Primary Examiner(s): Tucker, Wesley
• Application Number:
  US14/623,106
• Publication Number:
  US 20150235074A1
• Time in Patent Office: 1,023 Days
• CPC Class Codes: G06F 18/2148 characterised by the process organisation or structure, e.g. boosting cascade
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06V 10/467 Encoded features or binary features, e.g. local binary patterns [LBP]
• CPC Class Codes: G06V 10/7747 Organisation of the process, e.g. bagging or boosting
• CPC Class Codes: G06V 40/161 Detection; Localisation; Normalisation
• CPC Class Codes: G06V 40/172 Classification, e.g. identification

Abstract

A face detector training method, a face detection method, and apparatuses are provided. In the present invention, during a training phase, a flexible block based local binary pattern feature and a corresponding second classifier are constructed, appropriate second classifiers are searched for to generate multiple first classifiers, and multiple layers of first classifiers that are obtained by using a cascading method form a final face detector; and during a detection phase, face detection is performed on a to-be-detected image by using a first classifier or a face detector that is learned during a training process, so that a face is differentiated from a non-face, and a face detection result is combined and output.

Claims

1. A face detector training method implemented by a computer having a processor, the method comprising:

performing, by the processor, a training process by;

collecting face and non-face images as a training image sample set;

extracting a flexible block based local binary pattern (FBLBP) feature of the face and non-face images to form an FBLBP feature set; and

using the FBLBP feature and a GentleBoost algorithm to perform training, to obtain a first classifier, wherein the first classifier comprises several optimal second classifiers, and wherein each optimal second classifier is obtained by training using the GentleBoost algorithm;

repeating, by the processor, the training process to obtain multiple layers of first classifiers; and

cascading, by the processor, the multiple layers of first classifiers to form a face detector.

2. The method according to claim 1, wherein collecting the face and the non-face images as the training image sample set and extracting the FBLBP feature of the face and the non-face images to form the FBLBP feature set comprises:

constructing the FBLBP feature to represent co-occurrence information of a relative change of local grayscale of an image, wherein the FBLBP feature comprises several congruent rectangular blocks, a threshold, and a sign bit, wherein the several congruent rectangular blocks comprise one pivot block and at least one neighbor block, wherein the threshold is θ

₀ when the sign bit is −

1, wherein the threshold is θ

₁ when the sign bit is 1, and wherein θ

₀ and θ

₁ are real numbers; and

calculating a sketch value AvgInt_pivotblock of the pivot block in the FBLBP feature and a sketch value AvgInt_{neighborblockk} of each of the neighbor blocks in the FBLBP feature, wherein the sketch value AvgInt_pivotblock of the pivot block is represented by average grayscale of an area in which the pivot block is located, the sketch value AvgInt_{neighborblockk} of each of the neighbor blocks is represented by average grayscale of an area in which each of the neighbor blocks is located separately, and k is a sequence number of the k^th neighbor block;

calculating a differential result according to the sign bit, and comparing and quantizing the differential result and the threshold for each of the neighbor blocks;

converting a binary number that consists of a quantization result of each of the neighbor blocks into a decimal number; and

saving the decimal number to obtain a sketch value of the FBLBP feature.

3. The method according to claim 1, wherein in using the FBLBP feature and the GentleBoost algorithm to perform training, to obtain the first classifier, wherein the first classifier comprises the several optimal second classifiers, and each optimal second classifier is obtained by training using the GentleBoost algorithm, a calculation formula (1) of a second classifier is as follows:

4. The method according to claim 3, wherein the method further comprises obtaining, by calculation, the weight ω

_i of the i^th training image sample according to a formula (3) and a normalization formula (4), wherein the formula (3) is ω

_i=ω

_i×

e<sup>−

yifm(xr)</sup>, and the formula (4) is

5. The method according to claim 3, wherein the method further comprises obtaining, by calculation, a sketch value x of an FBLBP feature of each second classifier according to a formula (5), wherein the formula (5) is as follows:

6. The method according to claim 1, wherein in using the FBLBP feature and the GentleBoost algorithm to perform training to obtain the first classifier, wherein the first classifier comprises the several optimal second classifiers, a process of calculating the optimal second classifiers comprises the following substeps:

initially, the FBLBP feature comprises only one pivot block and one neighbor block, wherein an FBLBP feature set of the FBLBP feature that consists of the two rectangular blocks may be obtained using Brute force and traversal, for each FBLBP feature in the FBLBP feature set, calculating a sketch value of the FBLBP feature and an output value of a corresponding second classifier, substituting the obtained output value of the second classifier into a formula (6) to obtain an error J of the second classifier, and selecting a second classifier having a smallest value of the error J as the optimal second classifier, wherein the formula (6) is as follows;

7. The method according to claim 6, further comprising:

determining a threshold of the first classifier according to a formula (8), wherein the formula (8) is as follows;

8. The method according to claim 2, wherein for each of the neighbor blocks, calculating the differential result according to the sign bit, comparing and quantizing the differential result and the threshold, converting the binary number that consists of the quantization result of each of the neighbor blocks into the decimal number, and saving the decimal number to obtain the sketch value of the FBLBP feature comprises, for each neighbor block, when the sign bit is 1 and a difference between the sketch value AvgInt_pivotblock of the pivot block and a sketch value AvgInt_{neighborblockk} of the k^th neighbor block is not less than the threshold θ

₁, labeling a position of the neighbor block as 1, and otherwise, when the difference between the sketch value AvgInt_pivotblock of the pivot block and the sketch value AvgInt_{neighborblockk} of the k^th neighbor block is less than the threshold θ

₁, labeling the position of the neighbor block as 0, or for each of the neighbor blocks, when an opposite number of a difference between the sketch value AvgInt_pivotblock of the pivot block and a sketch value AvgInt_{neighborblockk} of the k^th neighbor block is not less than the threshold θ

₀, labeling a position of the neighbor block as 1, and otherwise, when the opposite number of the difference between the sketch value AvgInt_pivotblock of the pivot block and the sketch value AvgInt_{neighborblockk} of the k^th neighbor block is less than the threshold θ

₀, labeling the position of the neighbor block as 0.

9. A face detector training apparatus, comprising:

a memory comprising instructions; and

a processor coupled to the memory, wherein the instructions cause the processor to be configured to;

collect face and non-face images as a training image sample set;

extract a flexible block based local binary pattern (FBLBP) feature of the face and non-face images to form an FBLBP feature set;

perform training, using the FBLBP feature and using a GentleBoost algorithm, to obtain a first classifier, wherein the first classifier comprises several optimal second classifiers, and wherein each optimal second classifier is obtained by training using the GentleBoost algorithm;

repeat a training process to obtain multiple layers of first classifiers; and

cascade the multiple layers of first classifiers to form a face detector.

10. The apparatus according to claim 9, wherein the instructions further cause the processor to be configured to:

construct the FBLBP feature to represent co-occurrence information of a relative change of local grayscale of an image, wherein the FBLBP feature comprises several congruent rectangular blocks, a threshold, and a sign bit, wherein the several congruent rectangular blocks comprise one pivot block and at least one neighbor block, and when the sign bit is −

1, the threshold is θ

₀, and when the sign bit is 1, the threshold is θ

₁, wherein θ

₀ and θ

₁ are real numbers;

calculate a sketch value AvgInt_pivotblock of the pivot block in the FBLBP feature that is-a sketch value AvgInt_{neighborblockk} of each of the neighbor block in the FBLBP feature, wherein the sketch value AvgInt_pivotblock of the pivot block is represented by average grayscale of an area in which the pivot block is located, the sketch value AvgInt_{neighborblockk} of each of the neighbor blocks is represented by average grayscale of an area in which each of the neighbor blocks is located separately, and k is a sequence number of the k^th neighbor block, for each of the neighbor blocks;

calculate a differential result according to the sign bit;

compare and quantize the differential result and the threshold;

convert a binary number that consists of a quantization result of each of the neighbor blocks into a decimal number; and

save the decimal number to obtain a sketch value of the FBLBP feature.

11. The apparatus according to claim 9, wherein the instructions further cause the processor to be configured to use the FBLBP feature and the GentleBoost algorithm to perform training to obtain the first classifier, wherein the first classifier comprises several optimal second classifiers, and each optimal second classifier is obtained by training using the GentleBoost algorithm, wherein a calculation formula (1) of a second classifier is as follows:

12. The apparatus according to claim 11, wherein the instructions further cause the processor to be configured to obtain, by calculation, the weight ω

_i of the i^th training image sample according to a formula (3) and a normalization formula (4), wherein the formula (3) is ω

_i=ω

_i×

e<sup>−

yifm(xi)</sup>, and the formula (4) is

13. The apparatus according to claim 11, wherein the instructions further cause the processor to be configured to obtain, by calculation, a sketch value x of an FBLBP feature of each second classifier in the formula (1) according to a formula (5), wherein the formula (5) is as follows:

14. The apparatus according to claim 9, wherein the FBLBP feature comprises only one pivot block and one neighbor block, wherein an FBLBP feature set of the FBLBP feature that consists of the two rectangular blocks may be obtained using Brute force and traversal, for each FBLBP feature in the FBLBP feature set, wherein the first classifier is configured to:

calculate a sketch value of the FBLBP feature and an output value of a corresponding second classifier;

substitute the obtained output value of the second classifier into a formula (6) to obtain an error J of the second classifier;

select a second classifier having a smallest value of the error J as the optimal second classifier, wherein the formula (6) is as follows;

15. The apparatus according to claim 14, wherein the instructions further cause the processor to be configured to determine a threshold of the first classifier according to a formula (8), wherein the formula (8) is as follows:

16. The apparatus according to claim 10, wherein the instructions further cause the processor to be configured to, for each neighbor block, when the sign bit is 1 and a difference between the sketch value AvgInt_pivotblock of the pivot block and a sketch value AvgInt_{neighborblockk} of the k^th neighbor block is not less than the threshold θ

₁, label a position of the neighbor block as 1, and otherwise, when the difference between the sketch value AvgInt_pivotblock of the pivot block and the sketch value AvgInt_{neighborblockk} of the k^th neighbor block is less than the threshold θ

₁, label the position of the neighbor block as 0, or for each of the neighbor blocks, when an opposite number of a difference between the sketch value AvgInt_pivotblock of the pivot block and a sketch value AvgInt_{neighborblockk} of the k^th neighbor block is not less than the threshold θ

₀, label a position of the neighbor block as 1, and otherwise, when the opposite number of the difference between the sketch value AvgInt_pivotblock of the pivot block and the sketch value AvgInt_{neighborblockk} of the k^th neighbor block is less than the threshold θ

₀, label the position of the neighbor block as 0.