CN102156887A - Human face recognition method based on local feature learning - Google Patents
Human face recognition method based on local feature learning Download PDFInfo
- Publication number
- CN102156887A CN102156887A CN 201110075216 CN201110075216A CN102156887A CN 102156887 A CN102156887 A CN 102156887A CN 201110075216 CN201110075216 CN 201110075216 CN 201110075216 A CN201110075216 A CN 201110075216A CN 102156887 A CN102156887 A CN 102156887A
- Authority
- CN
- China
- Prior art keywords
- people
- face
- local
- sample
- histogram
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 239000013598 vector Substances 0.000 claims abstract description 21
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 230000019771 cognition Effects 0.000 claims description 6
- 230000001815 facial effect Effects 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 6
- 230000004069 differentiation Effects 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 4
- 230000008901 benefit Effects 0.000 abstract description 2
- 230000027928 long-term synaptic potentiation Effects 0.000 abstract 2
- 230000004044 response Effects 0.000 abstract 1
- 238000000638 solvent extraction Methods 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 7
- 238000012360 testing method Methods 0.000 description 7
- 230000008859 change Effects 0.000 description 5
- 238000005286 illumination Methods 0.000 description 4
- 238000000513 principal component analysis Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000011840 criminal investigation Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Landscapes
- Image Analysis (AREA)
- Collating Specific Patterns (AREA)
Abstract
The invention discloses a human face recognition method based on local feature learning, which comprises the following steps of: (a) partitioning a known classified human face sample into blocks, and calculating data of the human face sample of each block through an LBP (Length Between Perpendiculars) operator and an LTP (Long-Term Potentiation) operator to obtain a local histogram vector a of data of the human face sample of each block; (b) performing chi-square histogram distance calculation on local histogram vectors of the same positions on any two different human face samples of the same person in all human face samples to obtain a positive sample feature library; and (c) performing chi-square histogram distance calculation on local histogram vectors of the same positions on any two human face samples of different persons in all human face samples to obtain a negative sample feature library. The human face recognition method based on the local feature learning, provided by the invention, has the advantages of quick response, high accuracy and good recognition effect.
Description
Technical field
The present invention relates to a kind of biological identification technology, particularly a kind of face identification method based on local feature study.
Background technology
Biometrics identification technology is meant the technology of utilizing intrinsic physiological characteristic of biosome (mainly referring to the people) or behavioural characteristic to carry out identity authentication.With traditional identity authentication technology such as certificate, magnetic card, passwords etc. are compared, biometrics identification technology has made full use of individual's intrinsic biological characteristic, stopped the forgery of identity from the source and stolen, effective more, reliable, safety have obtained application more and more widely in information safety system.
In biometrics identification technology, compare iris recognition, fingerprint recognition etc., recognition of face has naturality, substantivity, friendly and advantage such as untouchable, be subjected to people's favor degree height, wide application prospect arranged at aspects such as national security, public security, criminal investigation, judicial domain, information security and Self-Services.
Traditional face identification method based on local feature study all can obtain certain recognition effect, but the recognition of face effect big to the illumination luminance difference, that expression shape change is apparent in view is not ideal enough.
Summary of the invention
To the objective of the invention is in order addressing the above problem, to have designed a kind of face identification method based on local feature study.
Realize that above-mentioned purpose technical scheme of the present invention is, a kind of face identification method based on local feature study, this method comprises sets up training pattern and two steps of model of cognition.
Wherein the foundation based on training pattern in the face identification method of local feature study may further comprise the steps:
(a) known classification is good people's face sample carries out the piecemeal processing, and by LBP operator (local binary pattern) and LTP operator (local three binarization modes) people's face sample data of every is calculated, and draws local histogram's vector of every people's face sample data;
(b) to the local histogram's vector side of card histogram distance calculation of the same position on any two the different people's face samples of same individual in everyone the face sample, obtain positive sample characteristics storehouse;
(c) to the local histogram's vector side of card histogram distance calculation of the same position on any two people's face samples of different people in everyone the face sample, obtain the negative sample feature database;
(d) will carry out computing in the input of the data in the positive and negative samples feature database AdaBoost cascade classifier, obtaining any two the people's face samples of differentiation is others' AdaBoost cascade classifier database of same class.
Foundation based on the model of cognition of the face identification method of local feature study may further comprise the steps;
(e) operation that facial image to be identified is carried out step (a) obtains local histogram's vector of every people's face sample data;
(f) local histogram's vector of every people's face sample data on the facial image to be identified and local histogram vector the block side histogram distance calculation of everyone face sample on same position in the AdaBoost cascade classifier database are obtained characteristic;
(g) will carry out computing in the described characteristic input AdaBoost cascade classifier and compare judgement with data in the AdaBoost cascade classifier database, the people's face classification under people's face sample that judgment data comes to the same thing be exactly the classification that people's face figure to be identified belongs to.
People's face sample data described in the more excellent step (a)-(e) is meant that the size of people's face sample and gray scale are carried out data normalization handles resulting data value.
The present invention compared with prior art has following beneficial effect: in extracting the face characteristic process, the face characteristic that utilizes LBP operator and LTP operator that the method for multiple local feature is extracted is more accurate, illumination, attitude variation are had good robustness, and discrimination is higher; The Adaboost cascade classifier is incorporated in the many classification problems of people's face, thereby utilization one-to-many strategy is converted into two class problems to the multiclass problem, has improved travelling speed, makes the recognition of face performance obtain significant raising.
Description of drawings
Fig. 1 is the schematic flow sheet based on the face identification method of local feature study that the present invention proposes;
Fig. 2 is basic LBP operator transformation synoptic diagram;
Fig. 3 is basic LTP operator transformation synoptic diagram;
Embodiment
For ease of the understanding of technical solution of the present invention, be introduced below in conjunction with concrete embodiment.As shown in Figure 1, a kind of face identification method based on local feature study, this method comprises sets up training pattern and two steps of model of cognition, wherein setting up training pattern may further comprise the steps: (a) that known classification is good people's face sample carries out the piecemeal processing, and by LBP operator and LTP operator people's face sample data of every is calculated, draw local histogram's vector of every people's face sample data; (b) to the local histogram's vector side of card histogram distance calculation of the same position on any two the different people's face samples of same individual in everyone the face sample, obtain positive sample characteristics storehouse; (c) to the local histogram's vector side of card histogram distance calculation of the same position on any two people's face samples of different people in everyone the face sample, obtain the negative sample feature database; (d) will carry out computing in the input of the data in the positive and negative samples feature database AdaBoost cascade classifier, obtaining any two the people's face samples of differentiation is others' AdaBoost cascade classifier database of same class.This method is set up model of cognition and be may further comprise the steps; (e) operation that facial image to be identified is carried out step (a) obtains local histogram's vector of every people's face sample data; (f) local histogram's vector of every people's face sample data on the facial image to be identified and local histogram vector the block side histogram distance calculation of everyone face sample on same position in the AdaBoost cascade classifier database are obtained characteristic; (g) will carry out computing in the described characteristic input AdaBoost cascade classifier and compare judgement with data in the AdaBoost cascade classifier database, the people's face classification under people's face sample that judgment data comes to the same thing be exactly the classification that people's face figure to be identified belongs to.
Be meant that in the people's face sample data described in the step (a)-(e) size of people's face sample and gray scale are carried out data normalization handles resulting data value, wherein the size of people's face sample is according to the center cutting of eyes, thereby and is meant the gray level image that the influence that utilized histogram equalization to operate to eliminate illumination obtains for the gray scale of people's face sample.Described histogram equalization operation is gray level γ in the hypothesis piece image
mThe probability that occurs is: p
γ(γ
m)=n
i/ n, m=0,1,2 ..., L-1, n are the summation of pixel in the image, n
mBe that gray level is γ
mNumber of pixels, L is the gray level sum in the image.Histogram equalization form with this understanding is:
Described LBP operator is local binary pattern operator, and the LTP operator is meant local three binarization mode operators.All characteristics that calculate by described LBP operator and LTP operator all are to represent with LBP operator and the corresponding decimal data of LTP operator, then by operation program AdaBoost cascade classifier, LBP operator and LTP operator characteristic of correspondence data are classified, obtain to distinguish people's face of same individual and be not the characteristic set of people's face of same individual.Simultaneously, the AdaBoost cascade classifier is converted into two person-to-person people's face classification problems with people's face classification problem of a plurality of people.The specific implementation method of described AdaBoost cascade classifier classification is:
1. given every layer maximum negative sample false drop rate f and minimum positive pattern detection rate d, the negative sample false drop rate target F that cascade classifier will reach
TarWith positive pattern detection rate D
Tar, P, N are respectively positive and negative sample set;
2. set F
0=1.0, D
0=1.0, i=0;
3. work as F
i＞F
TarThe time, i=i+1, n
i=0, F
i=F
I-1Work as F
i＞f * F
I-1The time, n
i=n
i+ 1.
4. in computing collection P and N, have the strong classifier of n feature with the computing of Adaboost algorithm; Calculate the F of current cascade classifier
iAnd D
iAdjust the threshold value of current strong classifier, up to the verification and measurement ratio of current cascade classifier more than or equal to d * D
I-1, N is nonempty set;
5. if F
i＞F
Tar, the negative sample image that the current cascade classifier that obtains is sorted in other is judged, there not being judicious image to put into N.
Wherein the specific implementation of the Adaboost algorithm in the 4th step is:
1) given n computing sample (x
1, y
1) ..., (x
n, y
n), y
i=0,1 represents x respectively
iBe negative sample or positive sample.
2) initializes weights
Wherein positive number of samples is l, and the negative sample number is m.
3) t is from 1 to T, and following steps are carried out in circulation:
A) normalized weight
B) to each feature f
j, Weak Classifier h of computing
j, remember that this sorter error rate is
C) from all Weak Classifiers that previous step calculates, find out and have lowest error rate ω
tSorter h
t
D) upgrade weight
β wherein
t=ε
t/ (1-ε
t), if x
iBe classified correctly e
i=0, otherwise e
i=1.
4) obtain strong classifier at last: if
H (x)=1 then; Otherwise h (x)=0.Wherein
Carry out of the inventive method on famous FERET face database test, compare with the face identification method of classics, the discrimination of this method improves a lot.As shown in table 1, the inventive method with based on the method for principal component analysis, carried out the test and appraisal comparison based on the method for Bayes with based on the method for elastic bunch graph coupling.Wherein Fb is the expression shape change test set, and Fc is the illumination variation test set, and Dup I and Dup II change test set the time, and wherein Dup II is that the time difference in change is that year changes test set.Draw from table, the inventive method obviously is better than additive method, and for example in expression shape change test set Fc, the inventive method can reach 0.947 on discrimination, is much better than method discrimination 0.658 and all the other two kinds of methods based on principal component analysis (PCA).On computational complexity, the present invention has introduced cascade Adaboost method, and its computational complexity also is starkly lower than traditional method.Therefore, the inventive method is compared with existing face identification method, has not only improved the robustness of recognition of face, and has reduced the computational complexity of recognition of face, makes the recognition of face effect obtain significant raising.
Table 1
Technique scheme has only embodied the optimal technical scheme of technical solution of the present invention, those skilled in the art to some part wherein some changes that may make all embodied principle of the present invention, belong within the protection domain of invention.
Claims (3)
1. the face identification method based on local feature study comprises and sets up training pattern and two steps of model of cognition, and the foundation of this method training pattern may further comprise the steps:
(a) known classification is good people's face sample carries out the piecemeal processing, and by LBP operator and LTP operator people's face sample data of every is calculated, and draws local histogram's vector of every people's face sample data;
(b) to the local histogram's vector side of card histogram distance calculation of the same position on any two the different people's face samples of same individual in everyone the face sample, obtain positive sample characteristics storehouse;
(c) to the local histogram's vector side of card histogram distance calculation of the same position on any two people's face samples of different people in everyone the face sample, obtain the negative sample feature database;
(d) will carry out computing in the input of the data in the positive and negative samples feature database AdaBoost cascade classifier, obtaining any two the people's face samples of differentiation is others' AdaBoost cascade classifier database of same class.
2. the face identification method based on local feature study according to claim 1, the foundation of this method model of cognition may further comprise the steps;
(e) operation that facial image to be identified is carried out step (a) obtains local histogram's vector of every people's face sample data;
(f) local histogram's vector of every people's face sample data on the facial image to be identified and local histogram vector the block side histogram distance calculation of everyone face sample on same position in the AdaBoost cascade classifier database are obtained characteristic;
(g) will carry out computing in the described characteristic input AdaBoost cascade classifier and compare judgement with data in the AdaBoost cascade classifier database, the people's face classification under people's face sample that judgment data comes to the same thing be exactly the classification that people's face figure to be identified belongs to.
3. the face identification method based on local feature study according to claim 1 is characterized in that, the people's face sample data described in the step (a)-(g) is meant that the size of people's face sample and gray scale are carried out data normalization handles resulting data value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110075216 CN102156887A (en) | 2011-03-28 | 2011-03-28 | Human face recognition method based on local feature learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110075216 CN102156887A (en) | 2011-03-28 | 2011-03-28 | Human face recognition method based on local feature learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102156887A true CN102156887A (en) | 2011-08-17 |
Family
ID=44438377
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201110075216 Pending CN102156887A (en) | 2011-03-28 | 2011-03-28 | Human face recognition method based on local feature learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102156887A (en) |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102324042A (en) * | 2011-09-13 | 2012-01-18 | 盛乐信息技术（上海）有限公司 | Visual recognition system and visual recognition method |
| CN102663426A (en) * | 2012-03-29 | 2012-09-12 | 东南大学 | Face identification method based on wavelet multi-scale analysis and local binary pattern |
| CN102867188A (en) * | 2012-07-26 | 2013-01-09 | 中国科学院自动化研究所 | Method for detecting seat state in meeting place based on cascade structure |
| CN103258190A (en) * | 2013-05-13 | 2013-08-21 | 苏州福丰科技有限公司 | Face recognition method used for mobile terminal |
| CN103761507A (en) * | 2014-01-03 | 2014-04-30 | 东南大学 | Local multi-value pattern face recognition method based on Weber law |
| CN103996018A (en) * | 2014-03-03 | 2014-08-20 | 天津科技大学 | Human-face identification method based on 4DLBP |
| CN104484650A (en) * | 2014-12-09 | 2015-04-01 | 北京信息科技大学 | Method and device for identifying sketch face |
| CN104700098A (en) * | 2015-04-01 | 2015-06-10 | 中国矿业大学(北京) | Dark environment face identification method based on improved Fisher face identification |
| WO2016082253A1 (en) * | 2014-11-26 | 2016-06-02 | 苏州福丰科技有限公司 | Three-dimensional face recognition method for entry and exit administration |
| CN105760865A (en) * | 2016-04-12 | 2016-07-13 | 中国民航大学 | Facial image recognizing method capable of increasing comparison correct rate |
| CN106250841A (en) * | 2016-07-28 | 2016-12-21 | 山东师范大学 | A kind of self-adaptive redundant dictionary construction method for recognition of face |
| CN106545266A (en) * | 2016-12-20 | 2017-03-29 | 武汉理工大学 | Vehicle window automatic switch device based on in-car detector and temperature sensor |
| CN107273855A (en) * | 2017-06-17 | 2017-10-20 | 南京理工大学 | Illumination invariant face identification method based on the linear Local Operator of higher derivative |
| CN105426894B (en) * | 2015-11-06 | 2018-08-14 | 中国铁道科学研究院 | Railway plug pin image detecting method and device |
| CN108764126A (en) * | 2018-05-25 | 2018-11-06 | 郑州目盼智能科技有限公司 | A kind of embedded living body faces tracking system |
| CN109002770A (en) * | 2018-06-25 | 2018-12-14 | 电子科技大学 | A kind of face identification method under the conditions of low resolution |
| CN109145559A (en) * | 2018-08-02 | 2019-01-04 | 东北大学 | A kind of intelligent terminal face unlocking method of combination Expression Recognition |
| CN112199986A (en) * | 2020-08-20 | 2021-01-08 | 西安理工大学 | A face image recognition method based on local binary pattern multi-distance learning |
| WO2022068487A1 (en) * | 2020-09-30 | 2022-04-07 | 北京字节跳动网络技术有限公司 | Styled image generation method, model training method, apparatus, device, and medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070237387A1 (en) * | 2006-04-11 | 2007-10-11 | Shmuel Avidan | Method for detecting humans in images |
| CN101393599A (en) * | 2007-09-19 | 2009-03-25 | 中国科学院自动化研究所 | A Game Character Control Method Based on Facial Expressions |
| CN101404060A (en) * | 2008-11-10 | 2009-04-08 | 北京航空航天大学 | Human face recognition method based on visible light and near-infrared Gabor information amalgamation |
| WO2009109127A1 (en) * | 2008-03-07 | 2009-09-11 | The Chinese University Of Hong Kong | Real-time body segmentation system |
| CN101561867A (en) * | 2009-05-19 | 2009-10-21 | 华中科技大学 | Human body detection method based on Gauss shape feature |
-
2011
- 2011-03-28 CN CN 201110075216 patent/CN102156887A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070237387A1 (en) * | 2006-04-11 | 2007-10-11 | Shmuel Avidan | Method for detecting humans in images |
| CN101393599A (en) * | 2007-09-19 | 2009-03-25 | 中国科学院自动化研究所 | A Game Character Control Method Based on Facial Expressions |
| WO2009109127A1 (en) * | 2008-03-07 | 2009-09-11 | The Chinese University Of Hong Kong | Real-time body segmentation system |
| CN101404060A (en) * | 2008-11-10 | 2009-04-08 | 北京航空航天大学 | Human face recognition method based on visible light and near-infrared Gabor information amalgamation |
| CN101561867A (en) * | 2009-05-19 | 2009-10-21 | 华中科技大学 | Human body detection method based on Gauss shape feature |
Non-Patent Citations (2)
| Title |
|---|
| 《模式识别与人工智能》 20061231 陈华杰等 一种基于RealAdaBoost算法的EOM人脸检测方法 第812-817页 1-3 第16卷, 第6期 * |
| 《计算机应用》 20050831 廖红文等 复用特征组合的单幅人脸图像识别 第1777-1779页 1-3 第25卷, 第8期 * |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102324042A (en) * | 2011-09-13 | 2012-01-18 | 盛乐信息技术（上海）有限公司 | Visual recognition system and visual recognition method |
| CN102663426A (en) * | 2012-03-29 | 2012-09-12 | 东南大学 | Face identification method based on wavelet multi-scale analysis and local binary pattern |
| CN102663426B (en) * | 2012-03-29 | 2013-12-04 | 东南大学 | Face identification method based on wavelet multi-scale analysis and local binary pattern |
| CN102867188B (en) * | 2012-07-26 | 2015-06-17 | 中国科学院自动化研究所 | Method for detecting seat state in meeting place based on cascade structure |
| CN102867188A (en) * | 2012-07-26 | 2013-01-09 | 中国科学院自动化研究所 | Method for detecting seat state in meeting place based on cascade structure |
| CN103258190A (en) * | 2013-05-13 | 2013-08-21 | 苏州福丰科技有限公司 | Face recognition method used for mobile terminal |
| CN103761507A (en) * | 2014-01-03 | 2014-04-30 | 东南大学 | Local multi-value pattern face recognition method based on Weber law |
| CN103761507B (en) * | 2014-01-03 | 2017-02-08 | 东南大学 | Local multi-value pattern face recognition method based on Weber law |
| CN103996018A (en) * | 2014-03-03 | 2014-08-20 | 天津科技大学 | Human-face identification method based on 4DLBP |
| CN103996018B (en) * | 2014-03-03 | 2018-03-02 | 天津科技大学 | Face identification method based on 4DLBP |
| WO2016082253A1 (en) * | 2014-11-26 | 2016-06-02 | 苏州福丰科技有限公司 | Three-dimensional face recognition method for entry and exit administration |
| CN104484650A (en) * | 2014-12-09 | 2015-04-01 | 北京信息科技大学 | Method and device for identifying sketch face |
| CN104484650B (en) * | 2014-12-09 | 2017-09-12 | 北京信息科技大学 | The method and apparatus of sketch recognition of face |
| CN104700098A (en) * | 2015-04-01 | 2015-06-10 | 中国矿业大学(北京) | Dark environment face identification method based on improved Fisher face identification |
| CN104700098B (en) * | 2015-04-01 | 2018-02-02 | 中国矿业大学(北京) | A kind of dark situation face identification method based on the identification of modified fischer face |
| CN105426894B (en) * | 2015-11-06 | 2018-08-14 | 中国铁道科学研究院 | Railway plug pin image detecting method and device |
| CN105760865A (en) * | 2016-04-12 | 2016-07-13 | 中国民航大学 | Facial image recognizing method capable of increasing comparison correct rate |
| CN106250841A (en) * | 2016-07-28 | 2016-12-21 | 山东师范大学 | A kind of self-adaptive redundant dictionary construction method for recognition of face |
| CN106250841B (en) * | 2016-07-28 | 2019-03-19 | 山东师范大学 | A kind of self-adaptive redundant dictionary construction method for recognition of face |
| CN106545266A (en) * | 2016-12-20 | 2017-03-29 | 武汉理工大学 | Vehicle window automatic switch device based on in-car detector and temperature sensor |
| CN107273855A (en) * | 2017-06-17 | 2017-10-20 | 南京理工大学 | Illumination invariant face identification method based on the linear Local Operator of higher derivative |
| CN108764126A (en) * | 2018-05-25 | 2018-11-06 | 郑州目盼智能科技有限公司 | A kind of embedded living body faces tracking system |
| CN108764126B (en) * | 2018-05-25 | 2021-09-07 | 郑州目盼智能科技有限公司 | Embedded living body face tracking system |
| CN109002770A (en) * | 2018-06-25 | 2018-12-14 | 电子科技大学 | A kind of face identification method under the conditions of low resolution |
| CN109002770B (en) * | 2018-06-25 | 2021-03-16 | 电子科技大学 | Face recognition method under low-resolution condition |
| CN109145559A (en) * | 2018-08-02 | 2019-01-04 | 东北大学 | A kind of intelligent terminal face unlocking method of combination Expression Recognition |
| CN112199986A (en) * | 2020-08-20 | 2021-01-08 | 西安理工大学 | A face image recognition method based on local binary pattern multi-distance learning |
| WO2022068487A1 (en) * | 2020-09-30 | 2022-04-07 | 北京字节跳动网络技术有限公司 | Styled image generation method, model training method, apparatus, device, and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102156887A (en) | Human face recognition method based on local feature learning | |
| Sun et al. | Graph matching iris image blocks with local binary pattern | |
| Mady et al. | Face recognition and detection using Random forest and combination of LBP and HOG features | |
| CN103049736A (en) | Face identification method based on maximum stable extremum area | |
| CN107944356B (en) | Identity authentication method for palmprint image recognition based on hierarchical topic model integrating multi-type features | |
| CN108564040B (en) | Fingerprint activity detection method based on deep convolution characteristics | |
| CN105469076A (en) | Face comparison and verification method based on multi-instance learning | |
| CN110555386A (en) | Face recognition identity authentication method based on dynamic Bayes | |
| Ramya et al. | Certain investigation on iris image recognition using hybrid approach of Fourier transform and Bernstein polynomials | |
| Ngxande et al. | Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms | |
| Dutta | Improving the recognition of faces using LBP and SVM optimized by PSO technique | |
| Nguyen et al. | Automatic identification fingerprint based on machine learning method | |
| CN111209850B (en) | Method for generating applicable multi-device identification finger vein image based on improved cGAN network | |
| Liu et al. | A novel high-resolution fingerprint representation method | |
| Lin et al. | Feature level fusion of fingerprint and finger vein biometrics | |
| Xiao et al. | An improved siamese network model for handwritten signature verification | |
| Bansal et al. | FAR and FRR based analysis of iris recognition system | |
| Li et al. | Face liveness detection based on parallel CNN | |
| CN103207993A (en) | Face recognition method based on nuclear distinguishing random neighbor embedding analysis | |
| Tsigie et al. | Face recognition techniques based on 2d local binary pattern, histogram of oriented gradient and multiclass support vector machines for secure document authentication | |
| CN105488493B (en) | A kind of quick iris identification method | |
| Aydoğdu et al. | A study on liveness analysis for palmprint recognition system | |
| Naim et al. | Classification of thumbprint using artificial neural network (ANN) | |
| Herlambang et al. | Cloud-based architecture for face identification with deep learning using convolutional neural network | |
| Tahmasebi et al. | Signature identification using dynamic and HMM features and KNN classifier |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| AD01 | Patent right deemed abandoned |
Effective date of abandoning: 20110817 |
|
| C20 | Patent right or utility model deemed to be abandoned or is abandoned |