CN104794464A - In vivo detection method based on relative attributes - Google Patents
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- CN104794464A CN104794464A CN201510243778.XA CN201510243778A CN104794464A CN 104794464 A CN104794464 A CN 104794464A CN 201510243778 A CN201510243778 A CN 201510243778A CN 104794464 A CN104794464 A CN 104794464A
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/165—Detection; Localisation; Normalisation using facial parts and geometric relationships
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/174—Facial expression recognition
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Abstract
The invention relates to an in vivo detection method based on relative attributes. The in vivo detection method comprises the following steps: 1) detecting a human face position on each frame of image in an input video; 2) obtaining human face key points; 3) obtaining an eye or mouth area according to the obtained key points; 4) judging whether the rule of attribute change values of the areas multiple continuous frames of images obtained in the step 3) meets the change rule of a true human face, if so, judging the human face as a true human face, and if not, judging the human face as a false human face. Compared with the prior art, the in vivo detection method provided by invention has the advantages of high detection accuracy, high speed, etc.
Description
Technical field
The present invention relates to a kind of human face detection tech, especially relate to a kind of biopsy method based on relative priority.
Background technology
Recognition of face succeeds in fields such as public security protection, work attendance gate inhibitions as a kind of identity identifying technology.But conventional face's recognition technology does not consider the true and false of target face, be therefore easily subject to the attack of false face.If false face success attack, likely causes heavy losses to user, therefore reliable and efficient face In vivo detection technology becomes the important component part of face verification system.
Conventional face's recognition technology often uses the methods such as Fourier analysis, blink detection and three dimensional depth estimation to judge live body.But these method difficulties meet the requirement of finance and public safety-security area.Mainly contain two factors: 1) these algorithms are difficult in performance meets rate of false alarm and be less than per mille and percent of pass will index more than 95%; 2) attack of the specific false face of difficult opposing, as the opposing of blink detection difficulty is attacked based on the false face of video.
The close one's eyes performance of (or opening one's mouth) sorter of tradition is difficult to satisfy the demands, such as, have eye less or have eye to close completely.
Summary of the invention
Object of the present invention be exactly in order to overcome above-mentioned prior art exist defect and provide a kind of accuracy of detection high, the fireballing biopsy method based on relative priority.
Object of the present invention can be achieved through the following technical solutions:
Based on a biopsy method for relative priority, comprise the following steps:
1) face location on every two field picture in input video is detected;
2) face key point is obtained;
3) region of eyes or face is obtained according to obtained key point;
4) judge step 3 in continuous multiple frames image) obtain the attribute change value in region rule whether meet the Changing Pattern of real human face, be if so, then judged as real human face, if not, be then judged as false face.
Described attribute change value is the distance changing value between upper lower eyelid or the distance changing value between upper lower lip.
Described step 4) be specially:
401) become one to scheme the eyes of present frame and front t frame or face region merging technique, adopt the homing method based on degree of depth study to export attribute change value in two two field pictures;
402) step 401 is repeated) until obtain the attribute change value of every two field picture;
403) by all properties changing value time sequencing composition one vector frame by frame, SVM classifier is utilized to classify to described vector;
404) judge whether classification results meets the Changing Pattern of the real human face under set action, is if so, then judged as real human face, if not, is then judged as false face.
Described step 403) in, before utilizing SVM classifier to classify to described vector, the length of each vector is set.
Described set action comprises closes one's eyes or opens one's mouth.
Described input video is one section of face video of 3 ~ 5 seconds.
Described step 1) adopt AdaBoost detection of classifier face location.
Described step 2) in, the detailed process obtaining face key point is:
201) mode utilizing HoG and SVM to combine carries out first round critical point detection, and each key point has K kind to select;
202) utilize global shape information, adopt N-Best mode, in K^N kind possibility, obtain composition face shape key point optimum solution, N is key point number, adopts branch-and-bound mode to carry out lopping process, obtains final form families;
203) calculation procedure 202) in obtain often kind combination degree of confidence, choose the combination that degree of confidence is high.
Described degree of confidence is made up of two parts:
A) step 201) in utilize HoG mode to obtain degree of confidence;
B) position relationship between different key point.
Compared with prior art, the present invention has the following advantages:
1) the present invention adopts attribute change information to carry out living body faces detection, compared to existing technology, performance has obvious lifting, meets rate of false alarm and is less than per mille and percent of pass will index more than 95%;
2) algorithm adopted in the inventive method has fireballing advantage, improves the detection rates of face, and the video processing 3 ~ 5 seconds only needs 0.5 second time;
3) the small-sized client of the present invention (as smart mobile phone etc.) can reach live effect;
4) the present invention carries out living body faces detection by attribute change information, can meet the demand that as less in eyes or eyes such as cannot to close completely at the occasion.
Accompanying drawing explanation
Fig. 1 is schematic flow sheet of the present invention.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.The present embodiment is implemented premised on technical solution of the present invention, give detailed embodiment and concrete operating process, but protection scope of the present invention is not limited to following embodiment.
The present embodiment provides a kind of biopsy method based on relative priority, have employed the mode of man-machine interaction, the specific action such as allow detected object close one's eyes, to open one's mouth, and by judging whether detected object completes these work, thus judges whether be real human face.As shown in Figure 1, this method comprises the following steps:
Step S1, adopt the face location on every two field picture in AdaBoost detection of classifier input video, input video is one section of face video of 3 ~ 5 seconds.
Step S2, obtain face key point, detailed process is:
201) mode utilizing HoG and SVM to combine carries out first round critical point detection, and each key point has K kind to select;
202) utilize global shape information, adopt N-Best mode, in K^N kind possibility, obtain composition face shape key point optimum solution, N is key point number, adopts branch-and-bound mode to carry out lopping process, obtains final form families;
203) calculation procedure 202) in obtain often kind combination degree of confidence, choose the combination that degree of confidence is high.This is required to meet Gaussian distribution, and this Gaussian distribution obtains according to statistics in advance.Degree of confidence is made up of two parts: a) step 201) in utilize HoG mode to obtain degree of confidence; B) position relationship between different key point.
Step S3, obtains the region of eyes or face according to obtained key point.
Step S4, judges step 3 in continuous multiple frames image) obtain the attribute change value in region rule whether meet the Changing Pattern of real human face, be if so, then judged as real human face, if not, be then judged as false face, be specially:
401) become one to scheme the eyes of present frame and front t frame or face region merging technique, adopt the homing method based on degree of depth study to export attribute change value in two two field pictures;
402) step 401 is repeated) until obtain the attribute change value of every two field picture, this attribute change value is the distance changing value between upper lower eyelid or the distance changing value between upper lower lip;
403) by all properties changing value time sequencing composition one vector frame by frame, SVM classifier is utilized to classify to described vector;
SVM can only process the proper vector of length-specific, and due to the speed issue of human action, proper vector length is inconsistent.Three kinds of modes are adopted to be fixed the vector of length: frame a) removing foremost; B) rearmost frame is removed; C) sample according to certain frequency.
404) judge classification results whether meet set action (as closed one's eyes or opening one's mouth) under the Changing Pattern of real human face, be if so, then judged as real human face, if not, be then judged as false face.If close one's eyes, distance changing value there will be from large to small, then the pattern of changing from small to big, and this is that the attack of false face cannot be simulated substantially.
Claims (9)
1., based on a biopsy method for relative priority, comprise the following steps:
1) face location on every two field picture in input video is detected;
2) face key point is obtained;
It is characterized in that, also comprise:
3) region of eyes or face is obtained according to obtained key point;
4) judge step 3 in continuous multiple frames image) obtain the attribute change value in region rule whether meet the Changing Pattern of real human face, be if so, then judged as real human face, if not, be then judged as false face.
2. the biopsy method based on relative priority according to claim 1, is characterized in that, described attribute change value is the distance changing value between upper lower eyelid or the distance changing value between upper lower lip.
3. the biopsy method based on relative priority according to claim 2, is characterized in that, described step 4) be specially:
401) become one to scheme the eyes of present frame and front t frame or face region merging technique, adopt the homing method based on degree of depth study to export attribute change value in two two field pictures;
402) step 401 is repeated) until obtain the attribute change value of every two field picture;
403) by all properties changing value time sequencing composition one vector frame by frame, SVM classifier is utilized to classify to described vector;
404) judge whether classification results meets the Changing Pattern of the real human face under set action, is if so, then judged as real human face, if not, is then judged as false face.
4. the biopsy method based on relative priority according to claim 3, is characterized in that, described step 403) in, before utilizing SVM classifier to classify to described vector, the length of each vector is set.
5. the biopsy method based on relative priority according to claim 3, is characterized in that, described set action comprises closes one's eyes or open one's mouth.
6. the biopsy method based on relative priority according to claim 1, is characterized in that, described input video is one section of face video of 3 ~ 5 seconds.
7. the biopsy method based on relative priority according to claim 1, is characterized in that, described step 1) adopt AdaBoost detection of classifier face location.
8. the biopsy method based on relative priority according to claim 1, is characterized in that, described step 2) in, the detailed process obtaining face key point is:
201) mode utilizing HoG and SVM to combine carries out first round critical point detection, and each key point has K kind to select;
202) utilize global shape information, adopt N-Best mode, in K^N kind possibility, obtain composition face shape key point optimum solution, N is key point number, adopts branch-and-bound mode to carry out lopping process, obtains final form families;
203) calculation procedure 202) in obtain often kind combination degree of confidence, choose the combination that degree of confidence is high.
9. the biopsy method based on relative priority according to claim 8, is characterized in that, described degree of confidence is made up of two parts:
A) step 201) in utilize HoG mode to obtain degree of confidence;
B) position relationship between different key point.
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Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105046227A (en) * | 2015-07-24 | 2015-11-11 | 上海依图网络科技有限公司 | Key frame acquisition method for human image video system |
| CN105139503A (en) * | 2015-10-12 | 2015-12-09 | 北京航空航天大学 | Lip moving mouth shape recognition access control system and recognition method |
| CN105243378A (en) * | 2015-11-13 | 2016-01-13 | 清华大学 | Method and device of living body face detection on the basis of eyes information |
| CN106557726A (en) * | 2015-09-25 | 2017-04-05 | 北京市商汤科技开发有限公司 | A kind of band is mourned in silence the system for face identity authentication and its method of formula In vivo detection |
| CN106557723A (en) * | 2015-09-25 | 2017-04-05 | 北京市商汤科技开发有限公司 | A kind of system for face identity authentication with interactive In vivo detection and its method |
| CN107330370A (en) * | 2017-06-02 | 2017-11-07 | 广州视源电子科技股份有限公司 | A kind of brow furrows motion detection method and device and vivo identification method and system |
| CN107330914A (en) * | 2017-06-02 | 2017-11-07 | 广州视源电子科技股份有限公司 | Face position method for testing motion and device and vivo identification method and system |
| CN107625527A (en) * | 2016-07-19 | 2018-01-26 | 杭州海康威视数字技术股份有限公司 | A kind of lie detecting method and device |
| CN108124486A (en) * | 2017-12-28 | 2018-06-05 | 深圳前海达闼云端智能科技有限公司 | Face living body detection method based on cloud, electronic device and program product |
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| CN108487820A (en) * | 2018-03-07 | 2018-09-04 | 常州信息职业技术学院 | A kind of novel electron detection closing device |
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| CN109389002A (en) * | 2017-08-02 | 2019-02-26 | 阿里巴巴集团控股有限公司 | Biopsy method and device |
| CN109522863A (en) * | 2018-11-28 | 2019-03-26 | 北京达佳互联信息技术有限公司 | Ear's critical point detection method, apparatus and storage medium |
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| US11551481B2 (en) | 2018-10-25 | 2023-01-10 | Tencent Technology (Shenzhen) Company Limited | Living body detection method and apparatus, electronic device, storage medium, and related system to which living body detection method is applied |
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| CN104794464B (en) | 2019-06-07 |
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