CN106295522A - A kind of two-stage anti-fraud detection method based on multi-orientation Face and environmental information - Google Patents

A kind of two-stage anti-fraud detection method based on multi-orientation Face and environmental information Download PDF

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CN106295522A
CN106295522A CN201610614086.6A CN201610614086A CN106295522A CN 106295522 A CN106295522 A CN 106295522A CN 201610614086 A CN201610614086 A CN 201610614086A CN 106295522 A CN106295522 A CN 106295522A
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face
background
orientation
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fraud detection
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CN106295522B (en
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李顺喜
王茜
陈卓
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Wuhan University of Technology WUT
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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
    • G06V40/161—Detection; Localisation; Normalisation
    • 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
    • G06V40/172—Classification, e.g. identification
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
    • G07C1/10—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people together with the recording, indicating or registering of other data, e.g. of signs of identity

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  • General Health & Medical Sciences (AREA)
  • Oral & Maxillofacial Surgery (AREA)
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Abstract

The invention discloses a kind of two-stage anti-fraud detection method based on multi-orientation Face and environmental information, comprise the following steps: the face in detection region is tracked, face is carried out multi-orientation detection, the facial image angularly key words sorting detected is stored；Background contrast region is determined according to the face location detected；The background under the background of current face and unmanned environment is contrasted in background contrast region；Judge whether the face that detects meets multi-orientation Face condition, it is judged that in background contrast region, whether the matching degree of the background under the background of current face and unmanned environment meets condition；Two conditions are then judged to when being satisfied by that currently detected face is real human face, otherwise judge that currently detected face is as human face photo or video；Store the positive face image in the multi-orientation Face detected.On Cao Zuo more friendly to user, can rapidly and efficiently carry out face anti-fraud detection, the safety and reliability for face identification system provides safeguard.

Description

A kind of two-stage anti-fraud detection method based on multi-orientation Face and environmental information
Technical field
The present invention relates to technical field of face recognition, be specifically related to a kind of based on multi-orientation Face with the two-stage of environmental information Anti-fraud detection method.
Background technology
In all of biometrics identification technology, recognition of face is a kind of biometric identity the most natural, visual Recognition method, meets the physiological vision custom of mankind itself, and mankind itself is through face to differentiate people's.In recent years, Face recognition technology develops rapidly and has obtained increasingly being widely applied, and the safety of recognition of face is also by more and more therewith Many concerns, spoofing attack is then the lethal challenge of face identification system.Through the development of decades, in field of face identification In, scholars have been proposed for the technology of a large amount of In vivo detection and method is applied to enterprise's work attendance recognition of face that user capacity is little In system, and the colleges and universities that place big for user capacity, that flow of the people is many, such as course are numerous, student's capacity is big, also do not propose A kind of preferably anti-fraud biopsy method.
Under normal conditions, face identification system be faced with three kinds of frauds:
1. use the photo of validated user；
2. use the video of validated user；
3. use the threedimensional model of validated user.
Using validated user photograph to carry out spoofing attack is the most cheap and convenient means, because human face photo is very easy to Obtain, such as surf the Net download, photographic head shooting etc..Video deception can present the live body movement effects identical with being really face, Such as motion of blinking, head movement, expression motion etc..And relative first two fraud, just manufacture the threedimensional model of a face Appear to much more complex, and the case that currently used this mode carries out spoofing attack to face identification system be the most fewer, So we the most temporarily cheat, just for photo and video, research of discussing.
Application publication number is " the interactive video biopsy method controlled based on human face posture of CN 105260726 A And system " proposing a kind of interactive video identification system, it is when carrying out Face datection, and user is required completion system institute Point out actions such as blinking or open one's mouth, use photo to carry out the user played tricks to filter.This system work attendance is applied significantly to carry The cost of high equipment, the In vivo detection long period to be spent to everyone, and strong depend-ence user coordinate, and are unfavorable for that user holds The place that amount is big, flow of the people is many is suitable for；Authorization Notice No. is the " a kind of based on the motion of face physiological of CN 100514353 C In-vivo detection method and system " propose a kind of In vivo detection system, this system is by the face physiological in detection region Real human face and photo face are distinguished in movable detection, and if this system is carried out video spoofing attack, just can crack it Biopsy method, allows system become unreliable；Application publication number is " a kind of face In vivo detection side of CN 103886301 A Method " in face In vivo detection grader be required for particular person and be individually trained, the corresponding people of the most each accredited personnel Face live body grader, this mode is only applicable to the less enterprise's work attendance of user capacity or the work attendance of special sector equally, when with When family amount is excessive, it is clear that be the most uneconomic to everyone one live body grader of independent training.
Summary of the invention
The technical problem to be solved in the present invention is, in view of the foregoing defects the prior art has, it is provided that a kind of based on many The two-stage anti-fraud detection method of angle face and environmental information, more friendly to user in operation, can rapidly and efficiently carry out Face anti-fraud detects, and the safety and reliability for face identification system provides safeguard..
The present invention solves that above-mentioned technical problem be the technical scheme is that
A kind of two-stage anti-fraud detection method based on multi-orientation Face and environmental information, comprises the following steps:
1) face in detection region is tracked, face is carried out multi-orientation detection, the facial image that will detect Angularly key words sorting stores；
2) background contrast region is determined according to the face location detected；
3) in background contrast region, contrast the background under the background of current face and unmanned environment；
4) judge whether the face detected meets multi-orientation Face condition, it is judged that current face in background contrast region Whether the matching degree of the background under background and unmanned environment meets condition；
5) when step 4) in two conditions be then judged to that currently detected face is real human face when being satisfied by, enter To next step, otherwise judge that currently detected face is as human face photo or video；
6) the positive face image in the multi-orientation Face detected is stored.
Connect technique scheme, in described step 1) in, it is to use Haar-like feature that face carries out multi-orientation detection Face is carried out with each two field picture of the many classification Adaboost algorithm adjusted based on the Weak Classifier face video to detecting Detection.
Connect technique scheme, in step 1) in, angularly facial image is divided three classes, including positive face type, half side Face type and full side face type, positive face type includes the scope with face front for zero point rotation of the coordinate system [-20 °, 20 °], half side Face type includes the scope with face front for zero point rotation of the coordinate system [-60 ° ,-20 °) and (20 °, 60 °], and full side face type includes With face front for the scope of zero point rotation of the coordinate system [-90 ° ,-60 °) and (60 °, 90 °].
Connecing technique scheme, the facial image detected is stored in a reservoir by Key-Value, the people in the range of positive face The value of face image is labeled as 1, and the value of the facial image in the range of half side face is labeled as 0, the face in the range of the face of full side The value of image is labeled as-1.
Connect technique scheme, described step 4) in, when detecting same face, in storage container, value is 1,0 ,-1 successively Value time, then judge detect that face meets multi-orientation Face condition.
Connect technique scheme, in described step 6) in, it is facial image when 1 that positive face image is value, works as judgement When the face detected is real human face, by positive face image storage and send into identification system.
Connect technique scheme, described step 3) in, background contrast region A is positioned at for the face left and right sides, background contrast Region does not comprise the region of ear and hair, and the height in background contrast region is identical with the height of face, and face width is d, The width of the background contrast region A of the face left and right sides is 1/8d～1/3d, background contrast region A and homonymy face edge away from From for 1/8d～1/3d.
Connect technique scheme, described step 4) in, in background contrast region under the background of current face and unmanned environment When background the match is successful rate is more than 70%, it is judged that for the match is successful, meeting background environment matching condition, otherwise it fails to match, It is unsatisfactory for background environment matching condition.
The method have the advantages that
The method that the present invention provides, is to prevent modal photo spoofing attack and the dual fail-safe of video spoofing attack, uses Family is walked close to turn round, after photographic head prepares detection and detected, the action left, and multi-orientation Face just can be detected, so this The method of kind is not required to user and coordinates, the most friendly to user, and background contrast also makes face anti-fraud detection work accurately and efficiently Can be quickly completed, be applicable not only to enterprise's work attendance that user capacity is little, be more suitable for user capacity big time work attendance, by multi-angle Face datection and background contrast combine, and just can obtain a kind of quick, efficient, to the detection of user-friendly face anti-fraud Method.
Accompanying drawing explanation
Fig. 1 is the flow process of two-stage anti-fraud detection method based on multi-orientation Face and environmental information in the embodiment of the present invention Figure；
Fig. 2 is multi-orientation Face sample graph in the embodiment of the present invention；
Fig. 3 is background contrast region in the embodiment of the present invention；
Detailed description of the invention
The present invention is described in detail with embodiment below in conjunction with the accompanying drawings.
With reference to shown in Fig. 1, in the embodiment that the present invention provides based on multi-orientation Face and the two-stage of environmental information Anti-fraud detection method, comprises the following steps:
1) face in detection region is tracked, face is carried out multi-orientation detection, the facial image that will detect Angularly key words sorting stores；
2) background contrast region is determined according to the face location detected；
3) in background contrast region, contrast the background under the background of current face and unmanned environment；
4) judge whether the face detected meets multi-orientation Face condition, it is judged that current face in background contrast region Whether the matching rate of the background under background and unmanned environment meets condition；
5) when step 4) in two conditions be then judged to that currently detected face is real human face when being satisfied by, enter To next step, otherwise judge that currently detected face is as human face photo or video；
6) the positive face image in the multi-orientation Face detected is stored.
Further, in described step 1) in, face is carried out multi-orientation detection be use Haar-like feature and based on Each two field picture of the many classification Adaboost algorithm that Weak Classifier the adjusts face video to detecting carries out Face datection.
Further, described facial image is the face video sub-frame processing detected to be formed.
Further, in step 1) in, angularly facial image is divided three classes, including positive face type, half side face type With full side face type, positive face type includes the scope with face front for zero point rotation of the coordinate system [-20 °, 20 °], half side face type Including for face front for the scope of zero point rotation of the coordinate system [-60 ° ,-20 °) and (20 °, 60 °], full side face type includes with people Face front is the scope of zero point rotation of the coordinate system [-90 ° ,-60 °) and (60 °, 90 °], as in figure 2 it is shown, the first positive face of behavior in Fig. 2 Type sample, the second behavior half side face type sample, the third line is full side face type sample.
Further, the facial image detected is stored in a reservoir by Key-Value, the facial image in the range of positive face Value be labeled as 1, the value of the facial image in the range of half side face is labeled as 0, the facial image in the range of the face of full side Value is labeled as-1；During whole detection, the face of user will be tracked, to prevent the midway user people in certification Face is switched.
Further, described step 4) in, when detecting same face, in storage container, occurrence is 1,0 ,-1 successively During value, then judge to detect that face meets multi-orientation Face condition, storage container occurs successively show that face is continuous print Rather than interrupt, also it is as the foundation determining whether real human face.
Further, in described step 6) in, it is facial image when 1 that positive face image is value, when judging to detect Face when being real human face, by positive face image storage and send into identification system.
Further, described step 3) in, as it is shown on figure 3, background contrast region A is positioned at for the face left and right sides, background Contrast district does not comprise the region of ear and hair, and the height in background contrast region is identical with the height of face, background contrast district The left and right sides width of territory A is the 1/6 of face width d, and background contrast region A is face width d with the distance at face edge 1/6.
Further, described step 4) in, in background contrast region under current face's background and unmanned environment background When being made into power more than 70%, it is judged that for the match is successful, meeting background environment matching condition, otherwise it fails to match, discontented instep Scape environments match condition.
Further, described step 4) in, the background of current face and the background under unmanned environment in background contrast region Rate that the match is successful, refers in multiple facial images currently detected under the face background of individual facial image and unmanned environment Background is in background contrast region, and the quantity that the match is successful accounting in the sum of current face's image is that the match is successful Rate.
Further, in individual facial image the matching degree of the background under the background of face and unmanned environment more than 70% Time, i.e. think that the Background matching of individual facial image is successful.
Further, extract the background area under current face background area and unmanned environment in background contrast region, will Rectangular histogram in two regions extracted is mated, when mating numerical value more than 0.7, it is judged that for the match is successful, otherwise mate Failure.
In one embodiment of the present of invention, the operation principle of the present invention:
Face in detection region is tracked, and detects the multi-orientation Face of user:
For each two field picture of input video, use Haar similar characteristics and the many classification adjusted based on Weak Classifier Adaboost algorithm detects face and to the face classification detected.
Adaboost algorithm is that Paul Viola and Michael Jones proposed in calendar year 2001.It is that a kind of iteration is calculated Method, its basic thought is to train same grader (Weak Classifier), then these different training for different training sets Obtain grader on collection to gather, constitute a final strong classifier.
Adaboost algorithm requires that Weak Classifier accuracy is more than 1/2.But in many classification problems, due to random guess Accuracy only has 1/k, and compared with two class AdaBoost algorithms, many classification AdaBoost algorithms are difficult to find that accuracy is more than 1/2 Weak Classifier, the Weak Classifier number that obtains in actual applications may be caused not enough, it is impossible to ensure to obtain the best strong Grader.
The many classification Adaboost algorithm adjusted based on Weak Classifier proposes: if both can be to Weak Classifier accuracy Requirement be reduced to more than 1/k, low-quality Weak Classifier can be generated by limiting again, ensure that and obtain sufficiently strong strong classification Device.
When training Weak Classifier, it is judged that the result of this Weak Classifier, in the classification of all samples belonging to a class together, just Really the sample of classification weights and whether score to other any type weights with greatly.If meeting this condition, proceed Weighed value adjusting and next iteration.If be unsatisfactory for, then the Weak Classifier being likely due to train is the best, can be at weights Re-training Weak Classifier in the case of constant, judges whether new Weak Classifier meets the condition described in top the most again, If meet entrance to adjust next time, it is unsatisfactory for then re-training Weak Classifier.Through the restriction of this condition, when T levels off to nothing Time poor, the certain score of correct label of classifying is many to the quantity of other kinds label, and the error rate of final grader levels off to 0。
Assume m sample sequence { (x1,y1),(x2,y2),…,(xm,ym), wherein yiFor classification, and yi∈ Y={1, 2 ..., K}, Weak Classifier ht(x).Foryi=P, P ∈ 1,2 ..., K}, Weak Classifier htX (), at P class sample In this classification, assigning to all kinds of probability is { p1,p2,…,pK, if there being a=P, then require pa>pb,b∈{1,2,…,a-1,a+ 1,…,K}.If being unsatisfactory for this condition, then re-training htX (), if until training classification htX () meets above-mentioned condition.
The face type detected is positive face, half side face or full side face by the present invention.Wherein positive face type includes with face Front is the scope of zero point rotation of the coordinate system [-20 °, 20 °], half side face type include with face front for zero point rotation of the coordinate system [- 60 ° ,-20 °) and (20 °, 60 °] scope, full side face type include with face front for zero point rotation of the coordinate system [-90 ° ,-60 °) (60 °, 90 °] scope.
Training grader specific requirement is as follows:
1) the true man's facial image in positive example sample (including positive face collection, half side face collection, full side face collection) should be as much as possible Contain and block with or without eyes and the changing factor at sex, age etc.；
2) firstly the need of setting up a positive face storehouse, a half side face storehouse and face storehouse, a full side.Use camera collection respectively 30 boy students and 30 schoolgirl's face head portraits, everyone corresponding 30 positive faces, 20 half side faces in a left side, 20 half side faces in the right side, 20 Left full side face, full side, 20 right sides face.Picking rate is 20fps, and image resolution ratio is 640 pixel × 480 pixels；
3) respectively from network and with 5000 non-face negative datas under camera collection varying environment；
4) according to eye location, all images are converted into the Gray Face image of 24 × 24 pixels；
5) facial image (600 positive faces, 800 half side faces, 800 full side faces) of 20 people and 3500 are randomly selected Non-face image is as training set, and remaining facial image and non-face image are all as test set；
6) according to above-mentioned many classification Adaboost algorithm one grader of training adjusted based on Weak Classifier, it is used for just distinguishing Face, half side face, full side face and non-face；
7) in order to detect the effect of said method, testing grader with test set, test result is as shown in table 1.
Table 1 multi-orientation Face testing result
Classification Positive face Half side face Full side face Non-face
Type judgment accuracy 95.62% 86.34% 90.45% 100.0%
After face being detected and classifying, the facial image detected is stored in a reservoir by Key-Value, positive face Value be 1, the value of half side face is 0, and the value of full side face is-1.
It is necessary to carry out current face's background and the contrast of unmanned environmental background after face being detected:
Traditional method be by Face datection before environmental background preserve, then with detection time face background carry out right Ratio.
But the place that, environmental background a lot of at flow of the people is easily changed, university researchers as big in student's capacity, face is examined Survey environment when carrying out and be likely occurred large change compared to the environment just having opened photographic head before Face datection, as detected Time user have people's process, empty environment to become the environment etc. occupying artificial abortion behind.At this moment a real human face is arisen that Judge into the situation of video human face by accident, because the real background of real human face differs with environmental background when just opening photographic head Sample, then background contrast will be failed.
Contrast district is the region that the face left and right sides does not comprise hair, and as shown in the a-quadrant of Fig. 3, width is 1/6d, model Enclosing for [rect.x-1/6d*2, rect.x-1/6d] and [rect.x+d+1/6d, rect.x+d+1/6d*2], height be face height Degree rect.Height, scope is [rect.y, rect.y+h], and wherein rect is rectangular area face being detected, and d is detection The width rect.width of the face rectangular area rect arrived.The height of the h face rectangular area rect for detecting is rect.height。
Then by the contrast district, left and right of face background and and the rectangular histogram of contrast district, left and right of environmental background carry out right Ratio, more than 0.7 after the Histogram Matching coefficient in [left region-left region] and [right region-right region] weights, then background contrast Success, otherwise background contrast's failure.
When the multi-orientation Face of a people being detected, i.e. store and the container of image occurring, value is 1,0 ,-1 successively During image key, system judges that this is the dynamic human face (living body faces or video human face) of multi-orientation Face to be detected rather than quiet State photo face.
Meanwhile, when multi-orientation Face being detected, it is calculated background contrast's success rate of these multi-orientation Faces, when this When background contrast's success rate of a little frame faces is more than 70%, represent environments match success, detect be not video human face and It it is living body faces.
In sum, face In vivo detection is the indispensable part of face identification system, the property of face In vivo detection Can the quality of energy, decide face identification system and move towards actual application from theoretical research, and the method that the present invention provides, is to prevent Modal photo spoofing attack and the dual fail-safe of video spoofing attack, user walks close to photographic head and prepares detection and detected After turn round the action left, multi-orientation Face just can be detected, be in this way not required to user coordinate, the most friendly to user Good.And background contrast also makes face anti-fraud detection work to be quickly completed accurately and efficiently, it is applicable not only to user capacity little Enterprise's work attendance, be more suitable for user capacity big time work attendance.
The above presently preferred embodiments of the present invention that is only, can not limit the interest field of the present invention with this certainly, Therefore the equivalence change made according to scope of the present invention patent, still belongs to protection scope of the present invention.

Claims (8)

1. a two-stage anti-fraud detection method based on multi-orientation Face and environmental information, it is characterised in that include following step Rapid:
1) face in detection region is tracked, face is carried out multi-orientation detection, by the facial image that detects by angle Degree key words sorting stores；
2) background contrast region is determined according to the face location detected；
3) in background contrast region, contrast the background under the background of current face and unmanned environment；
4) judge whether the face detected meets multi-orientation Face condition, it is judged that the background of current face in background contrast region The match is successful whether rate meets background environment matching condition with the background under unmanned environment；
5) when step 4) in two conditions be then judged to that currently detected face is real human face when being satisfied by, enter into down One step, otherwise judges that currently detected face is as human face photo or video；
6) the positive face image in the multi-orientation Face detected is stored.
Two-stage anti-fraud detection method based on multi-orientation Face and environmental information the most according to claim 1, its feature It is, in described step 1) in, it is to use Haar-like feature and adjust based on Weak Classifier that face carries out multi-orientation detection Each two field picture of many classification Adaboost algorithm face video to detecting carry out Face datection.
Two-stage anti-fraud detection method based on multi-orientation Face and environmental information the most according to claim 1, its feature It is, in step 1) in, angularly facial image is divided three classes, including positive face type, half side face type and full side face type, Positive face type includes the scope with face front for zero point rotation of the coordinate system [-20 °, 20 °], and half side face type includes with face front For the scope of zero point rotation of the coordinate system [-60 ° ,-20 °) and (20 °, 60 °], full side face type includes with face front for zero point coordinate Rotate the scope of [-90 ° ,-60 °) and (60 °, 90 °].
Two-stage anti-fraud detection method based on multi-orientation Face and environmental information the most according to claim 3, its feature Being, the facial image detected is stored in a reservoir by Key-Value, the value labelling of the facial image in the range of positive face Being 1, the value of the facial image in the range of half side face is labeled as 0, and the value of the facial image in the range of the face of full side is labeled as- 1。
Two-stage anti-fraud detection method based on multi-orientation Face and environmental information the most according to claim 4, its feature It is, described step 4) in, when detecting same face, when storing in container the value that occurrence successively is 1,0 ,-1, then sentence Break and detect that face meets multi-orientation Face condition.
Two-stage anti-fraud detection method based on multi-orientation Face and environmental information the most according to claim 4, its feature It is, in described step 6) in, it is facial image when 1 that positive face image is value, when judging that the face detected is as true During face, by positive face image storage and send into identification system.
Two-stage anti-fraud detection method based on multi-orientation Face and environmental information the most according to claim 1, its feature It is, described step 3) in, background contrast region A is positioned at for the face left and right sides, and background contrast region does not comprise ear and head The region sent out, the height in background contrast region is identical with the height of face, and face width is d, in the background of the face left and right sides The width of contrast district A is 1/8d～1/3d, and background contrast region A is 1/8d～1/3d with the distance at homonymy face edge.
Two-stage anti-fraud detection method based on multi-orientation Face and environmental information the most according to claim 1, its feature It is, described step 4) in, the match is successful the rate of the background under the background of current face and unmanned environment in background contrast region During more than 70%, it is determined that the match is successful, meeting background environment matching condition, otherwise it fails to match.
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CN112069917A (en) * 2020-08-14 2020-12-11 武汉轻工大学 Face recognition system for fixed scene
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CN113177437A (en) * 2017-06-13 2021-07-27 阿里巴巴集团控股有限公司 Face recognition method and device
CN113553990A (en) * 2021-08-09 2021-10-26 深圳智必选科技有限公司 Method and device for tracking and identifying multiple faces, computer equipment and storage medium
CN114360036A (en) * 2022-03-21 2022-04-15 深圳市海清视讯科技有限公司 Image recognition method, device, server and storage medium
US11501531B2 (en) 2020-03-03 2022-11-15 Cyberlink Corp. Systems and methods for anti-spoofing protection using motion detection and video background analysis

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050276452A1 (en) * 2002-11-12 2005-12-15 Boland James M 2-D to 3-D facial recognition system
CN101702198A (en) * 2009-11-19 2010-05-05 浙江大学 Identification method for video and living body faces based on background comparison
CN102426646A (en) * 2011-10-24 2012-04-25 西安电子科技大学 Multi-angle human face detection device and method
CN102609695A (en) * 2012-02-14 2012-07-25 上海博物馆 Method and system for recognizing human face from multiple angles
CN102819733A (en) * 2012-08-09 2012-12-12 中国科学院自动化研究所 Rapid detection fuzzy method of face in street view image
CN103971092A (en) * 2014-04-09 2014-08-06 中国船舶重工集团公司第七二六研究所 Facial trajectory tracking method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050276452A1 (en) * 2002-11-12 2005-12-15 Boland James M 2-D to 3-D facial recognition system
CN101702198A (en) * 2009-11-19 2010-05-05 浙江大学 Identification method for video and living body faces based on background comparison
CN102426646A (en) * 2011-10-24 2012-04-25 西安电子科技大学 Multi-angle human face detection device and method
CN102609695A (en) * 2012-02-14 2012-07-25 上海博物馆 Method and system for recognizing human face from multiple angles
CN102819733A (en) * 2012-08-09 2012-12-12 中国科学院自动化研究所 Rapid detection fuzzy method of face in street view image
CN103971092A (en) * 2014-04-09 2014-08-06 中国船舶重工集团公司第七二六研究所 Facial trajectory tracking method

Cited By (33)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107093191A (en) * 2017-03-06 2017-08-25 阿里巴巴集团控股有限公司 A kind of verification method of image matching algorithm, device and computer-readable storage medium
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CN113177437A (en) * 2017-06-13 2021-07-27 阿里巴巴集团控股有限公司 Face recognition method and device
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CN107229927A (en) * 2017-08-03 2017-10-03 河北工业大学 A kind of Face datection anti-fraud method
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WO2019071739A1 (en) * 2017-10-13 2019-04-18 平安科技（深圳）有限公司 Face living body detection method and apparatus, readable storage medium and terminal device
CN107798742A (en) * 2017-10-31 2018-03-13 深圳市中科智诚科技有限公司 A kind of strong intelligent Time Attendance Device of recognition capability with automatic regulation function
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US11393267B2 (en) 2018-05-11 2022-07-19 Boe Technology Group Co., Ltd. Method and device for identifying visitor and access control system
WO2019214401A1 (en) * 2018-05-11 2019-11-14 京东方科技集团股份有限公司 Visitor recognition method and apparatus, and access control system
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