CN107609494A - A kind of human face in-vivo detection method and system based on silent formula - Google Patents

A kind of human face in-vivo detection method and system based on silent formula Download PDF

Info

Publication number
CN107609494A
CN107609494A CN201710773992.5A CN201710773992A CN107609494A CN 107609494 A CN107609494 A CN 107609494A CN 201710773992 A CN201710773992 A CN 201710773992A CN 107609494 A CN107609494 A CN 107609494A
Authority
CN
China
Prior art keywords
live
picture
vivo detection
detected
human
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
Application number
CN201710773992.5A
Other languages
Chinese (zh)
Inventor
许靳昌
董远
白洪亮
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Faceall Co
Original Assignee
Beijing Faceall Co
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beijing Faceall Co filed Critical Beijing Faceall Co
Priority to CN201710773992.5A priority Critical patent/CN107609494A/en
Publication of CN107609494A publication Critical patent/CN107609494A/en
Pending legal-status Critical Current

Links

Abstract

The invention discloses a kind of human face in-vivo detection method and system based on silent formula, human face in-vivo detection method includes step:Obtain decision model;Multistage live body judgement is carried out to picture to be detected according to the decision model.It is fast to realize detection speed, amount of calculation is small, and cost is low, silent formula, without just can voluntarily carry out face In vivo detection by hardware device;Can install in the form of software with mobile phone or computer, and then simply realize face In vivo detection, have a wide range of applications meaning;Corresponding decision model is obtained by SVMs and deep learning training, it is possible to achieve threshold value differentiates and two discriminant classifications, it is achieved thereby that carrying out live body judgement to the face in photo;Realize that multistage face live body judges by judgment models, and then realize that picture surface is analyzed, and improve the confidence level of live body judgement.Face In vivo detection system includes:Model acquiring unit and judging unit, realize human face in-vivo detection method identical beneficial effect.

Description

A kind of human face in-vivo detection method and system based on silent formula
Technical field
The present invention relates to face In vivo detection field, and in particular to a kind of human face in-vivo detection method based on silent formula and System.
Background technology
With the development of computer technology, the research to face recognition technology also deepens continuously;Wherein, face In vivo detection Technology has been widely used as a kind of technology that can identify true man.In general, face In vivo detection is to be directed to People in detection photo, the view data such as video recording whether be true man technology.
Nowadays, face In vivo detection uses the interactive mode of instruction type mostly, by voice message action command, such as blinks Eye, shake the head, open one's mouth, whether being true man judge participation detection activity, the problems such as speed is slow, participant mismatches being present. How in the case where reducing the instruction of these speech types while again not by hardware device, and it can quickly and accurately enter pedestrian Face In vivo detection is problem to be solved by this invention.
Existing face In vivo detection technology is mainly as follows:
Based on the method for interactive random action, shortcoming:Detection speed is slow, user mismatches, and interactivity is poor;Based on three-dimensional Image modeling technology, shortcoming:It is computationally intensive, it is necessary to 3D cameras, can not be met under current most of scenes;Using infrared Camera, shortcoming:Cost is high, requires harsh to device hardware condition, current most of mobile phone terminals do not have infrared camera, do not have Be widely used meaning.
The content of the invention
It is an object of the invention to provide a kind of human face in-vivo detection method and system based on silent formula, to solve above-mentioned lack Point.
To achieve these goals, the present invention provides following technical scheme:
The invention provides a kind of human face in-vivo detection method based on silent formula, comprise the following steps:
Obtain decision model,
The decision model comprises at least:Face accounting, SVMs, mobile phone frame pattern and false proof model;
Multistage live body judgement is carried out to picture to be detected according to the decision model,
If it is determined that being face live body, then the picture input next stage to be detected is subjected to live body judgement, be otherwise determined as Non-face live body.
Above-mentioned human face in-vivo detection method, the acquisition of the face accounting comprise the following steps:
Position to obtain the face frame of the picture to be detected by Face datection;
Percentage of the area of the face frame in the area of the picture to be detected is calculated, as the face Accounting.
Above-mentioned human face in-vivo detection method, the training of the SVMs comprise the following steps:
Collection obtains the positive negative sample clearly and obscured, and with the reflective reflective sample of minute surface;
Study is trained to the SVMs by above-mentioned sample, obtains Fuzzy Threshold and reflective threshold value.
Above-mentioned human face in-vivo detection method, the acquisition of the mobile phone frame pattern comprise the following steps:
Collection obtains the frame sample with bounding box features;
Deep learning is carried out according to the frame sample, obtains mobile phone frame pattern.
Above-mentioned human face in-vivo detection method, the acquisition of the false proof model comprise the following steps:
Collection obtains the dielectric sample with dielectric attribute;
Deep learning is carried out according to the dielectric sample, obtains false proof model.
Above-mentioned human face in-vivo detection method, picture to be detected progress live body judgement is included according to the face accounting following Step:
Accounting threshold value is set, and the face accounting is compared with accounting threshold value;
If being more than or equal to the accounting threshold value, it is determined as face live body, and is inputted next stage and carries out live body judgement, If being less than the accounting threshold value, it is determined as non-face live body.
Above-mentioned human face in-vivo detection method, according to the SVMs to picture to be detected carry out live body judge include with Lower step:
Laplace transform is carried out to the picture to be detected, and the mean square deviation of pixel value is calculated;
By the mean square deviation with by training Fuzzy Threshold that the SVMs obtains and reflective threshold value to be compared;
If being more than above-mentioned threshold value, it is determined as face live body, and is inputted next stage and carries out live body judgement, if less than upper Threshold value is stated, then is determined as face live body.
Above-mentioned human face in-vivo detection method, live body is carried out to picture to be detected according to the mobile phone frame pattern and judges to include Following steps:
Judge whether there is mobile phone frame in the picture to be detected by the mobile phone frame pattern;
If nothing, it is determined as face live body, and is inputted next stage and carries out live body judgement, if so, is then determined as face Live body.
Above-mentioned human face in-vivo detection method, picture to be detected progress live body judgement is included according to the false proof model following Step:
Judge in the picture to be detected whether to be face in medium by the false proof model;
If it is not, being then determined as face live body, and it is inputted next stage and carries out live body judgement, if so, is then determined as inhuman Face live body.
In above-mentioned technical proposal, the invention provides the human face in-vivo detection method based on silent formula, has beneficial below Effect:1) realize that detection speed is fast, and amount of calculation is small, cost is low, coordinates (silent formula), without by hardware device without user It just can voluntarily carry out face In vivo detection;2) the face In vivo detection technology can be installed and mobile phone or electricity in the form of software Brain, and then face In vivo detection simply is realized, have a wide range of applications meaning;3) instructed by SVMs and deep learning Practice and obtain corresponding decision model, it is possible to achieve threshold value differentiates and two discriminant classifications, it is achieved thereby that entering to the face in photo Row live body judges;4) realize that multistage face live body judges by judgment models, and then realize that picture surface is analyzed, and improve The confidence level that live body judges.
Present invention also offers a kind of face In vivo detection system based on silent formula, including:
Model acquiring unit, to obtain decision model,
The decision model comprises at least:Face accounting, SVMs, mobile phone frame and false proof model;
Judging unit, to carry out multistage live body judgement to picture to be detected according to the decision model,
If it is determined that being face live body, then the picture input next stage to be detected is subjected to live body judgement, be otherwise determined as Non-face live body.
In above-mentioned technical proposal, present invention also offers the face In vivo detection system based on silent formula, has with following Beneficial effect:1) realize that detection speed is fast, and amount of calculation is small, cost is low, coordinates (silent formula), without being set by hardware without user It is standby just voluntarily to carry out face In vivo detection;2) the system can be installed and mobile phone or computer, Jin Erjian in the form of software Single realizes face In vivo detection, has a wide range of applications meaning;3) phase is obtained by SVMs and deep learning training The decision model answered, it is possible to achieve threshold value differentiates and two discriminant classifications, sentences it is achieved thereby that carrying out live body to the face in photo It is disconnected;4) realize that multistage face live body judges by judgment models, and then realize that picture surface is analyzed, and improve live body judgement Confidence level.
Brief description of the drawings
, below will be to institute in embodiment in order to illustrate more clearly of the embodiment of the present application or technical scheme of the prior art The accompanying drawing needed to use is briefly described, it should be apparent that, drawings in the following description are only one described in the present invention A little embodiments, for those of ordinary skill in the art, other accompanying drawings can also be obtained according to these accompanying drawings.
Fig. 1 is the schematic flow sheet of human face in-vivo detection method provided in an embodiment of the present invention;
Fig. 2 is the schematic flow sheet for the human face in-vivo detection method that one embodiment of the present invention provides;
Fig. 3 is the schematic flow sheet for the human face in-vivo detection method that one embodiment of the present invention provides;
Fig. 4 is the schematic flow sheet for the human face in-vivo detection method that one embodiment of the present invention provides;
Fig. 5 is the schematic flow sheet for the human face in-vivo detection method that one embodiment of the present invention provides;
Fig. 6 is the schematic flow sheet for the human face in-vivo detection method that one embodiment of the present invention provides;
Fig. 7 is the schematic flow sheet for the human face in-vivo detection method that one embodiment of the present invention provides;
Fig. 8 is the schematic flow sheet for the human face in-vivo detection method that one embodiment of the present invention provides;
Fig. 9 is the schematic flow sheet for the human face in-vivo detection method that one embodiment of the present invention provides;
Figure 10 is the FB(flow block) for the human face in-vivo detection method that one embodiment of the present invention provides;
Figure 11 is the structural representation of face In vivo detection system provided in an embodiment of the present invention.
Embodiment
In order that those skilled in the art more fully understands technical scheme, below in conjunction with accompanying drawing to this hair It is bright to be further detailed.
It is a kind of human face in-vivo detection method based on silent formula provided in an embodiment of the present invention as shown in Fig. 1,11, bag Include following steps:
S101, decision model is obtained,
S1011, the decision model comprise at least:Face accounting, SVMs, mobile phone frame pattern and anti-pseudonorm Type;
The mode of the acquisition of decision model can be to be obtained by calculating, or by training grader to obtain.It is specific and Speech, face accounting obtains by calculating area accounting of the face in whole photo, SVMs be by clearly obscuring, Minute surface it is reflective wait photo training grader and obtain, mobile phone frame pattern for by the photo training grader with mobile phone frame and Obtain, false proof model is to be obtained by the photo training grader with medium;Above-mentioned each model is to be directed to In vivo detection During the attack meanses being commonly encountered and the processing model generated, each means is filtered by above-mentioned model.It is preferred that , it is in the present embodiment the human face in-vivo detection method based on single width photo, when the sample to be detected of input is collection of photographs When, priority treatment is carried out to the picture inputted at first on the time according to input principle at first, then handle other pictures successively;When defeated When the sample to be detected entered is video data, the image of each frame can be handled according to the playing sequence of video, until arriving video counts According to last frame embody;When the sample to be detected of input is single picture, is directly handled according to step, judge that it is No is face live body.Further, to picture carry out attack meanses filtration treatment when, according to above-mentioned model order successively Handled.
As shown in Fig. 2 in step S101 and S1011, the acquisition of the face accounting comprises the following steps:
S201, position by Face datection to obtain the face frame of the picture to be detected;
S202, percentage of the area of the face frame in the area of the picture to be detected is calculated, as institute State face accounting.
Specifically, be directed to some hand-held photos, identity card picture, the photo in work card photo or mobile phone etc., lead to Cross the face accounting analyzed in above-mentioned photo and obtain a decision model;A photo to be detected is inputted, can be determined by Face datection Position is to the face frame position in the photo to be detected, then calculates the area of the face frame and account for the percentage of photo to be detected, as Face accounting.
As shown in figure 3, in step S101 and S1011, the training of the SVMs comprises the following steps:
S301, collection obtain clear and fuzzy positive negative sample, and with the reflective reflective sample of minute surface;
S302, study is trained to the SVMs by above-mentioned sample, obtains Fuzzy Threshold and reflective threshold value.
Specifically, for some attack meanses, for example human face photo, during the camera, common web takes the photograph As head does not have a focusing function, obtain picture and have distortion, smudgy, by gathering picture distortion, fuzzy positive negative sample, then Depth training study is carried out to the SVMs, obtains Fuzzy Threshold;Attacked for papery photo, the liquid crystal such as mobile phone screen Screen attack, it is contemplated that above-mentioned attack meanses for photo reflective it is relatively good, the picture surface of acquisition has one layer of mirror Face is reflective;Collection has reflective reflective sample, then depth training study is carried out to the SVMs, obtains reflective threshold Value.Further, for photograph print, black-and-white photograph etc., because this kind of attack meanses have obvious color character, Wo Mengen The feature of this kind of attack is counted as a judgement according to pixel value.
As shown in figure 4, in step S101 and S1011, the acquisition of the mobile phone frame pattern comprises the following steps:
S401, collection obtain the frame sample with bounding box features;
S402, according to the frame sample carry out deep learning, obtain mobile phone frame pattern.
Specifically, this step is the reinforcement again for mobile phone attack, because some mobile phones attack picture face area It is very big, and reflecting effect also unobvious, but this kind of attack graph piece has mobile phone bounding box features, we pass through oneself shooting and received Collect the photo of mobile phone attack, using deep learning, train two disaggregated models, judge whether there is the presence of mobile phone in photo.
As shown in figure 5, in step S101 and S1011, the acquisition of the false proof model comprises the following steps:
S501, collection obtain the dielectric sample with dielectric attribute;
S502, according to the dielectric sample carry out deep learning, obtain false proof model.
Specifically, by collecting various photograph prints, mobile phone screen do not have the photo of frame, computer screen photo etc. these Different medium, learn the feature of dielectric surface by deep learning, obtain a false proof model, and then judge that the face in photo is Face in true man's face or these media.
S102, multistage live body judgement carried out to picture to be detected according to the decision model,
S1021, if it is determined that be face live body, then the picture input next stage to be detected is subjected to live body judgement, otherwise It is determined as non-face live body.
As shown in fig. 6, in step S102 and S1021, live body is carried out to picture to be detected according to the face accounting and sentenced It is disconnected to comprise the following steps:
S601, setting accounting threshold value, and the face accounting is compared with accounting threshold value;
If S602, being more than or equal to the accounting threshold value, it is determined as face live body, and is inputted next stage and carries out live body Judge, if being less than the accounting threshold value, be determined as non-face live body.
Specifically, as preferable in the present embodiment, accounting threshold value set according to the photo of different resolution, resolution Rate is bigger, and accounting threshold value is smaller, and as resolution ratio is into multiple increase, accounting threshold value can be in the reduction of identical multiple;Than Such as, for the photo of 320 × 240 this resolution ratio, if face accounts for the threshold value that screen gives than being less than us, it is determined as inhuman Face live body, we are just construed as attacking;If being more than the threshold value, be determined as face live body, then and enter next stage live body Judge.
As shown in fig. 7, in step S102 and S1021, live body is carried out to picture to be detected according to the SVMs Judgement comprises the following steps:
S701, Laplace transform is carried out to the picture to be detected, and the mean square deviation of pixel value is calculated;
S702, by the mean square deviation with by training Fuzzy Threshold that the SVMs obtains and reflective threshold value to carry out Compare;
If S703, being more than above-mentioned threshold value, it is determined as face live body, and is inputted next stage and carries out live body judgement, if Less than above-mentioned threshold value, then it is determined as face live body.
Specifically, by doing Laplace transform to photo to be detected, the picture after being converted, the photo is then calculated The mean square deviation of pixel value, by the mean square deviation with by training Fuzzy Threshold that the SVMs obtains and reflective threshold value to enter Row compares, if being more than above-mentioned threshold value, is determined as face live body, and is inputted next stage and carries out live body judgement, if less than upper Threshold value is stated, then is determined as face live body.
As shown in figure 8, in step S102 and S1021, picture to be detected is lived according to the mobile phone frame pattern Body judges to comprise the following steps:
S801, by the mobile phone frame pattern judge whether there is mobile phone frame in the picture to be detected;
If S802, nothing, being determined as face live body, and it is inputted next stage and carries out live body judgement, if so, is then determined as Face live body.
As shown in figure 9, in step S102 and S1021, live body is carried out to picture to be detected according to the false proof model and sentenced It is disconnected to comprise the following steps:
S901, by the false proof model judge in the picture to be detected whether to be face in medium;
S902, if it is not, be then determined as face live body, and be inputted next stage and carry out live body judgement, if so, being then determined as Non-face live body.
Specifically, the biopsy method in the present invention, at least require that all detections in above-mentioned steps will be by, The face just calculated in photo to be detected is determined as live body, has reached the confidence level lifting for being determined as face live body or non-face live body Purpose.Further, other decision models can also be introduced in an iterative manner, are carried out human body live body judgement, are made to be judged as The confidence level of face live body or non-face live body is further lifted.
As shown in figure 11, as preferably, unlatching camera obtains a photo to be detected first, uses in the present embodiment Face datection detects face, obtains the position of face frame, calculates the ratio that human face region accounts for whole photo, obtains face accounting, Continue next judgement if the face accounting is more than accounting threshold value, if less than returning result non-living body if accounting threshold value;It is next Step, according to the face frame detected before, face is intercepted, do Laplace transform, the image after calculating Laplace transform Pixel value mean square deviation, compared with the Fuzzy Threshold and reflex threshold got continuously respectively with grandson, if both greater than above-mentioned threshold value, under continuing One judgement, otherwise, returning result non-living body;Then, the photo to be detected is zoomed into certain yardstick, such as 64 × 64, lead to Mobile phone frame pattern is crossed, is differentiated with the presence or absence of there is mobile phone frame in photo, if mobile phone frame in judgment result displays photo be present, Then returning result non-living body;If there is no mobile phone frame, continue the judgement of next step.The human face region picture that will be intercepted before, Incoming dielectric model, judgement are true man's picture or non-true man's picture of some attack types, returning result.Finally, treat for one Picture is detected all by above-mentioned judgement, is just calculated and is determined as face live body.
In above-mentioned technical proposal, a kind of human face in-vivo detection method based on silent formula provided by the invention, have following Beneficial effect:
1) realize that detection speed is fast, and amount of calculation is small, cost is low, coordinates (silent formula), without by hardware without user Equipment just can voluntarily carry out face In vivo detection;
2) the face In vivo detection technology can install in the form of software with mobile phone or computer, and then simply realize people Face In vivo detection, has a wide range of applications meaning;
3) corresponding decision model is obtained by SVMs and deep learning training, it is possible to achieve threshold value differentiates and two Discriminant classification, it is achieved thereby that carrying out live body judgement to the face in photo;
4) realize that multistage face live body judges by judgment models, and then realize that picture surface is analyzed, and improve work The confidence level that body judges.
As shown in Figure 10, it is a kind of face In vivo detection system based on silent formula provided in an embodiment of the present invention, including: Model acquiring unit, to obtain decision model, the decision model comprises at least:Face accounting, SVMs, mobile phone side Frame and false proof model;Judging unit, to carry out multistage live body judgement to picture to be detected according to the decision model, if sentencing It is set to face live body, then the picture input next stage to be detected is subjected to live body judgement, is otherwise determined as non-face live body.
Specifically, the mode of the acquisition of decision model can be to be obtained by calculating, or by training grader to obtain. Specifically, face accounting is obtained by calculating area accounting of the face in whole photo, and SVMs is by clear Clear fuzzy, the photo training grader such as minute surface is reflective and obtain, mobile phone frame pattern is to pass through the photo training with mobile phone frame Grader and obtain, false proof model is obtains by the photo training grader with medium;Above-mentioned each model is to be directed to Each means was carried out by the attack meanses being commonly encountered during In vivo detection and the processing model generated by above-mentioned model Filter.Preferably, it is in the present embodiment the human face in-vivo detection method based on single width photo, when the sample to be detected of input is photograph During piece set, priority treatment is carried out to the picture inputted at first on the time according to input principle at first, then handle other figures successively Piece;When the sample to be detected of input is video data, the image of each frame can be handled according to the playing sequence of video, until arriving The last frame of video data embodies;When the sample to be detected of input is single picture, is directly handled, sentenced according to step Breaking, whether it is face live body.Further, when carrying out the filtration treatment of attack meanses to picture, according to the suitable of above-mentioned model Sequence is handled successively.After obtaining face accounting, accounting threshold value is set, and the face accounting is compared with accounting threshold value It is right;If being more than or equal to the accounting threshold value, it is determined as face live body, and is inputted next stage and carries out live body judgement, if small In the accounting threshold value, then it is determined as non-face live body;Obscured, after reflective threshold value, the picture to be detected is drawn Laplace transform, and the mean square deviation of pixel value is calculated;By the mean square deviation and by training the SVMs to obtain Fuzzy Threshold and reflective threshold value be compared;If being more than above-mentioned threshold value, it is determined as face live body, and be inputted next stage Live body judgement is carried out, if being less than above-mentioned threshold value, is determined as face live body;After obtaining mobile phone frame pattern, pass through the mobile phone Frame pattern judges whether there is mobile phone frame in the picture to be detected;If nothing, it is determined as face live body, and be inputted down One-level carries out live body judgement, if so, being then determined as face live body;After obtaining false proof model, institute is judged by the false proof model Whether state in picture to be detected is face in medium;If it is not, then it is determined as face live body, if so, being then determined as non-face work Body.
In above-mentioned technical proposal, a kind of face In vivo detection system based on silent formula provided by the invention, have following Beneficial effect:
1) realize that detection speed is fast, and amount of calculation is small, cost is low, coordinates (silent formula), without by hardware without user Equipment just can voluntarily carry out face In vivo detection;
2) the system can install in the form of software with mobile phone or computer, and then simply realize face In vivo detection, Have a wide range of applications meaning;
3) corresponding decision model is obtained by SVMs and deep learning training, it is possible to achieve threshold value differentiates and two Discriminant classification, it is achieved thereby that carrying out live body judgement to the face in photo;
4) realize that multistage face live body judges by judgment models, and then realize that picture surface is analyzed, and improve work The confidence level that body judges.
Some one exemplary embodiments of the present invention are only described by way of explanation above, undoubtedly, for ability The those of ordinary skill in domain, without departing from the spirit and scope of the present invention, can be with a variety of modes to institute The embodiment of description is modified.Therefore, above-mentioned accompanying drawing and description are inherently illustrative, should not be construed as to the present invention The limitation of claims.

Claims (10)

1. a kind of human face in-vivo detection method based on silent formula, it is characterised in that comprise the following steps:
Obtain decision model,
The decision model comprises at least:Face accounting, SVMs, mobile phone frame pattern and false proof model;
Multistage live body judgement is carried out to picture to be detected according to the decision model,
If it is determined that being face live body, then the picture input next stage to be detected is subjected to live body judgement, be otherwise determined as inhuman Face live body.
2. human face in-vivo detection method according to claim 1, it is characterised in that the acquisition of the face accounting include with Lower step:
Position to obtain the face frame of the picture to be detected by Face datection;
Percentage of the area of the face frame in the area of the picture to be detected is calculated, is accounted for as the face Than.
3. human face in-vivo detection method according to claim 1, it is characterised in that the training of the SVMs includes Following steps:
Collection obtains the positive negative sample clearly and obscured, and with the reflective reflective sample of minute surface;
Study is trained to the SVMs by above-mentioned sample, obtains Fuzzy Threshold and reflective threshold value.
4. human face in-vivo detection method according to claim 1, it is characterised in that the acquisition bag of the mobile phone frame pattern Include following steps:
Collection obtains the frame sample with bounding box features;
Deep learning is carried out according to the frame sample, obtains mobile phone frame pattern.
5. human face in-vivo detection method according to claim 1, it is characterised in that the acquisition of the false proof model include with Lower step:
Collection obtains the dielectric sample with dielectric attribute;
Deep learning is carried out according to the dielectric sample, obtains false proof model.
6. human face in-vivo detection method according to claim 1 or 2, it is characterised in that treated according to the face accounting Detection picture carries out live body and judges to comprise the following steps:
Accounting threshold value is set, and the face accounting is compared with accounting threshold value;
If being more than or equal to the accounting threshold value, it is determined as face live body, and is inputted next stage and carries out live body judgement, if small In the accounting threshold value, then it is determined as non-face live body.
7. the human face in-vivo detection method according to claim 1 or 3, it is characterised in that according to the SVMs pair Picture to be detected carries out live body and judges to comprise the following steps:
Laplace transform is carried out to the picture to be detected, and the mean square deviation of pixel value is calculated;
By the mean square deviation with by training Fuzzy Threshold that the SVMs obtains and reflective threshold value to be compared;
If being more than above-mentioned threshold value, it is determined as face live body, and is inputted next stage and carries out live body judgement, if is less than above-mentioned threshold Value, then be determined as face live body.
8. the human face in-vivo detection method according to claim 1 or 4, it is characterised in that according to the mobile phone frame pattern Live body is carried out to picture to be detected to judge to comprise the following steps:
Judge whether there is mobile phone frame in the picture to be detected by the mobile phone frame pattern;
If nothing, it is determined as face live body, and is inputted next stage and carries out live body judgement, if so, is then determined as face live body.
9. human face in-vivo detection method according to claim 1 or 5, it is characterised in that treated according to the false proof model Detection picture carries out live body and judges to comprise the following steps:
Judge in the picture to be detected whether to be face in medium by the false proof model;
If it is not, being then determined as face live body, and it is inputted next stage and carries out live body judgement, if so, is then determined as non-face work Body.
A kind of 10. face In vivo detection system based on silent formula, it is characterised in that including:
Model acquiring unit, to obtain decision model,
The decision model comprises at least:Face accounting, SVMs, mobile phone frame and false proof model;
Judging unit, to carry out multistage live body judgement to picture to be detected according to the decision model,
If it is determined that being face live body, then the picture input next stage to be detected is subjected to live body judgement, be otherwise determined as inhuman Face live body.
CN201710773992.5A 2017-08-31 2017-08-31 A kind of human face in-vivo detection method and system based on silent formula Pending CN107609494A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710773992.5A CN107609494A (en) 2017-08-31 2017-08-31 A kind of human face in-vivo detection method and system based on silent formula

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710773992.5A CN107609494A (en) 2017-08-31 2017-08-31 A kind of human face in-vivo detection method and system based on silent formula

Publications (1)

Publication Number Publication Date
CN107609494A true CN107609494A (en) 2018-01-19

Family

ID=61055583

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710773992.5A Pending CN107609494A (en) 2017-08-31 2017-08-31 A kind of human face in-vivo detection method and system based on silent formula

Country Status (1)

Country Link
CN (1) CN107609494A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108509916A (en) * 2018-03-30 2018-09-07 百度在线网络技术(北京)有限公司 Method and apparatus for generating image
CN109190522A (en) * 2018-08-17 2019-01-11 浙江捷尚视觉科技股份有限公司 A kind of biopsy method based on infrared camera
CN109376608A (en) * 2018-09-26 2019-02-22 中国计量大学 A kind of human face in-vivo detection method
CN110276313A (en) * 2019-06-25 2019-09-24 网易(杭州)网络有限公司 Identity identifying method, identification authentication system, medium and calculating equipment

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103605958A (en) * 2013-11-12 2014-02-26 北京工业大学 Living body human face detection method based on gray scale symbiosis matrixes and wavelet analysis
CN104766063A (en) * 2015-04-08 2015-07-08 宁波大学 Living body human face identifying method
CN105354554A (en) * 2015-11-12 2016-02-24 西安电子科技大学 Color and singular value feature-based face in-vivo detection method
CN105389554A (en) * 2015-11-06 2016-03-09 北京汉王智远科技有限公司 Face-identification-based living body determination method and equipment
CN106412158A (en) * 2016-09-28 2017-02-15 努比亚技术有限公司 Character photographing method and device
CN106599772A (en) * 2016-10-31 2017-04-26 北京旷视科技有限公司 Living body authentication method, identity authentication method and device
CN106951869A (en) * 2017-03-22 2017-07-14 腾讯科技(深圳)有限公司 A kind of live body verification method and equipment

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103605958A (en) * 2013-11-12 2014-02-26 北京工业大学 Living body human face detection method based on gray scale symbiosis matrixes and wavelet analysis
CN104766063A (en) * 2015-04-08 2015-07-08 宁波大学 Living body human face identifying method
CN105389554A (en) * 2015-11-06 2016-03-09 北京汉王智远科技有限公司 Face-identification-based living body determination method and equipment
CN105354554A (en) * 2015-11-12 2016-02-24 西安电子科技大学 Color and singular value feature-based face in-vivo detection method
CN106412158A (en) * 2016-09-28 2017-02-15 努比亚技术有限公司 Character photographing method and device
CN106599772A (en) * 2016-10-31 2017-04-26 北京旷视科技有限公司 Living body authentication method, identity authentication method and device
CN106951869A (en) * 2017-03-22 2017-07-14 腾讯科技(深圳)有限公司 A kind of live body verification method and equipment

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
刘华成: ""人脸活体检测关键技术研究"", 《中国优秀硕士学位论文全文数据库(电子期刊)信息科技辑》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108509916A (en) * 2018-03-30 2018-09-07 百度在线网络技术(北京)有限公司 Method and apparatus for generating image
CN109190522A (en) * 2018-08-17 2019-01-11 浙江捷尚视觉科技股份有限公司 A kind of biopsy method based on infrared camera
CN109190522B (en) * 2018-08-17 2021-05-07 浙江捷尚视觉科技股份有限公司 Living body detection method based on infrared camera
CN109376608A (en) * 2018-09-26 2019-02-22 中国计量大学 A kind of human face in-vivo detection method
CN110276313A (en) * 2019-06-25 2019-09-24 网易(杭州)网络有限公司 Identity identifying method, identification authentication system, medium and calculating equipment

Similar Documents

Publication Publication Date Title
CN107609494A (en) A kind of human face in-vivo detection method and system based on silent formula
JP6401873B2 (en) Region recognition method and apparatus
WO2017181769A1 (en) Facial recognition method, apparatus and system, device, and storage medium
CN104143086B (en) Portrait compares the application process on mobile terminal operating system
US9436862B2 (en) Electronic apparatus with segmented guiding function and small-width biometrics sensor, and guiding method thereof
da Silva Pinto et al. Video-based face spoofing detection through visual rhythm analysis
Liu A camera phone based currency reader for the visually impaired
CN105117706B (en) Image processing method and device, character identifying method and device
WO2018086543A1 (en) Living body identification method, identity authentication method, terminal, server and storage medium
CN104754216B (en) A kind of photographic method and device
WO2018040307A1 (en) Vivo detection method and device based on infrared visible binocular image
CN109376608B (en) Human face living body detection method
CN105389574B (en) The method and system of human eye iris in a kind of detection picture
CN105718863A (en) Living-person face detection method, device and system
WO2019127262A1 (en) Cloud end-based human face in vivo detection method, electronic device and program product
CN101266647A (en) Eyelid detection apparatus, eyelid detection method and program therefor
WO2014088125A1 (en) Image photographing device and method for same
CN107977636A (en) Method for detecting human face and device, terminal, storage medium
CN106296665A (en) Card image obscures detection method and device
CN108363944A (en) Recognition of face terminal is double to take the photograph method for anti-counterfeit, apparatus and system
WO2018192448A1 (en) People-credentials comparison authentication method, system and camera
CN108596041A (en) A kind of human face in-vivo detection method based on video
CN108647600A (en) Face identification method, equipment and computer readable storage medium
CN105657252B (en) Image processing method and mobile terminal in a kind of mobile terminal
CN102831430B (en) Method for predicting photographing time point and device adopting same

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20180119