CN102622588B - Dual-certification face anti-counterfeit method and device - Google Patents
Dual-certification face anti-counterfeit method and device Download PDFInfo
- Publication number
- CN102622588B CN102622588B CN2012100594547A CN201210059454A CN102622588B CN 102622588 B CN102622588 B CN 102622588B CN 2012100594547 A CN2012100594547 A CN 2012100594547A CN 201210059454 A CN201210059454 A CN 201210059454A CN 102622588 B CN102622588 B CN 102622588B
- Authority
- CN
- China
- Prior art keywords
- face
- people
- target
- imaging
- lambda
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 58
- 238000001514 detection method Methods 0.000 claims abstract description 33
- 230000001815 facial effect Effects 0.000 claims description 67
- 238000003384 imaging method Methods 0.000 claims description 52
- 238000001228 spectrum Methods 0.000 claims description 47
- 239000013598 vector Substances 0.000 claims description 47
- 230000000875 corresponding effect Effects 0.000 claims description 40
- 238000012549 training Methods 0.000 claims description 36
- 238000002310 reflectometry Methods 0.000 claims description 20
- 238000000701 chemical imaging Methods 0.000 claims description 15
- 238000009877 rendering Methods 0.000 claims description 11
- 238000002604 ultrasonography Methods 0.000 claims description 10
- 230000003993 interaction Effects 0.000 claims description 8
- 238000010801 machine learning Methods 0.000 claims description 8
- 230000009471 action Effects 0.000 claims description 7
- 238000003331 infrared imaging Methods 0.000 claims description 7
- 239000000284 extract Substances 0.000 claims description 6
- 238000003333 near-infrared imaging Methods 0.000 claims description 4
- 230000000007 visual effect Effects 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 3
- 230000004071 biological effect Effects 0.000 abstract description 2
- 238000001727 in vivo Methods 0.000 abstract 3
- 238000010586 diagram Methods 0.000 description 14
- 238000005516 engineering process Methods 0.000 description 11
- 210000000887 face Anatomy 0.000 description 10
- 230000006698 induction Effects 0.000 description 9
- 238000012706 support-vector machine Methods 0.000 description 8
- 238000012795 verification Methods 0.000 description 7
- 210000003128 head Anatomy 0.000 description 6
- 230000033001 locomotion Effects 0.000 description 6
- 230000003595 spectral effect Effects 0.000 description 5
- 238000000605 extraction Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000013475 authorization Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000003909 pattern recognition Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 241001527902 Aratus Species 0.000 description 1
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000001574 biopsy Methods 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000000193 eyeblink Effects 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000004313 glare Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000000741 silica gel Substances 0.000 description 1
- 229910002027 silica gel Inorganic materials 0.000 description 1
- 238000001429 visible spectrum Methods 0.000 description 1
Images
Classifications
-
- 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
- G06V40/40—Spoof detection, e.g. liveness detection
- G06V40/45—Detection of the body part being alive
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/10—Image acquisition
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Human Computer Interaction (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Collating Specific Patterns (AREA)
Abstract
The invention relates to a dual-certification face anti-counterfeit method and a dual-certification face anti-counterfeit device. The method comprises the following steps of 1, performing in-vivo detection on an acquired target face, judging whether the target face has biological activity, and if the target face is determined to have an in-vivo characteristic, stepping into the step 2; 2, during face identification application, calculating the similarity between the acquired target face and a face corresponding to an identification result, and if the similarity is greater than a certain threshold value, determining that the target face is a real effective face; and during face certification application, calculating the similarity between the acquired target face and a face corresponding to an identity claimed by the target face, and if the similarity is greater than a certain threshold value, determining that the target face is a real effective face. By the method, in-vivo detection is combined with identity certification, so that an accurate and reliable face anti-counterfeit detection result can be supplied.
Description
Technical field
The present invention relates to a kind of people's face method for anti-counterfeit and device, the especially a kind of pair of identifier's face method for anti-counterfeit and device belong to image and handle technical field with pattern-recognition.
Background technology
People's face anti-counterfeiting technology is related to the security of recognition of face authentication and authorization system, if nobody's face antiforge function, the recognition of face authentication and authorization system is vulnerable to the attack of false people's face, and then may cause serious safety problem.For example, the assailant can obtain the facial image of a certain specific objective (being the nominator) and make photo, video or mask etc. by certain means, is presented in face of the system, in the hope of obtaining illegal authority.Therefore, people's face anti-counterfeiting technology receives increasing concern.Existing people's face anti-counterfeiting technology in the world at present, mainly based on the man-machine interaction strategy: system sends specific instruction, requires the user to make specific behaviors such as nictation, pronunciation, and then judges the activity of input people face.Can be divided into following three kinds of modes according to common action: first kind of live body that is based on nictation detects, the document that discloses this technology has: 1) Gang Pan, Lin Sun, Zhaohui Wu and Shilong Lao.Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera, International Conference on Computer Vision, 2007,2) K.Kollreider, H.Fronthaler and J.Bigun.Verifying Liveness by Multiple Experts in Face Biometrics, IEEE Conference on Computer Vision and Pattern Recognition Workshop, 2008,3) patent No. is ZL200710178088.6, and denomination of invention is the patent documentation of " a kind of biopsy method and system based on the motion of people's face physiological ".Second kind is based on the live body of shaking the head and detects, the pertinent literature that discloses this technology comprises: 1) K.Kollreider, H.Fronthaler and J.Bigun.Evaluating Liveness by Face Images and the Structure Tensor, IEEE Workshop on Automatic Identification Advanced Technologies, 2005,2) Wei Bao, Hong Li, Nan Li and Wei Jiang.A Liveness Detection Method for Face Recognition Based on Optical Flow Field, International Conference on Image Analysis and Signal Processing, 2009.The third live body that is based on voice and mouth action detects, the pertinent literature that discloses this technology has: G.Chetty and M.Wagner.Liveness Verification in Audio-Video Speaker Authentication.In 10th Australian Int.Conference on Speech Science and Technology, 2004.
This method based on man-machine interaction is owing to require the user to show specific behavior, thus burden for users is heavier, the user experience not good, required time is longer.
In addition, the researcher who has starts with from multispectral angle, carrying out live body by the reflectivity of analyzing skin under different spectrum detects, pertinent literature has: 1) Ioannis Pavlidis, Peter Symosek, The Imaging Issue in an Automatic Face/Disguise Detection System, IEEE workshop on Computer Vision Beyond the Visible Spectrum:Methods and Applications, 2000.2)Youngshin?Kim，Jaekeun?Na，Seongbeak?Yoon，and?Juneho?Yi.Masked?fake?face?detection?using?radiance?measurements，J.Opt.Soc.Am，vol.26，no.4，April?2009。But this kind method is also very coarse at present, and is also unsatisfactory on the precision, also has very big room for improvement.
Above-described people's face method for anti-counterfeit can also become the live body detection technique, because they only judge whether biologically active of target people face.Yet, in the practical application, the situation that true personnel go the bogus attack nominator might appear, and this moment, target people face was real human face really, but still belonged to the behavior of attacking face identification system.Therefore people's face anti-counterfeiting technology should only not comprise the live body detection.And said method ubiquity burden for users is heavy, the man-machine interaction time is long, the not high shortcoming of accuracy, and it is imperative therefore to develop people's face method for anti-counterfeit accurate, quick, applied widely.
Summary of the invention
The objective of the invention is to overcome the deficiencies in the prior art, a kind of pair of identifier's face method for anti-counterfeit and device are provided, it improves identification accuracy, and is convenient, safe and reliable.
According to technical scheme provided by the invention, a kind of pair of identifier's face method for anti-counterfeit, described method comprises:
If in people's face checking is used, then calculate the similarity between the corresponding people's face of identity that target people's face of collecting and target people face claim, as if greater than a certain threshold value, think that then this target people face is authentic and valid people's face,
Wherein step 1 is irrelevant with the nominator, and step 2 is relevant with the nominator, and after the checking by step 1 and step 2 simultaneously of target people face, just can be identified as is authentic and valid people's face, is false people's face otherwise be identified as.
If described pair of identifier's face method for anti-counterfeit is based on visible light, then step 1 further comprises:
Have only target people face simultaneously by step 101 and 102, just be considered to detect by the live body of step 1.
Step 203 if in face recognition application, if the target facial image facial image corresponding with recognition result, is regarded as by the sorter in the step 202 and to be belonged to same people, thinks that then target people face is authentic and valid, otherwise is false people's face;
If in people's face checking is used, the target facial image facial image corresponding with nominator's identity of claiming then regarded as by the sorter in the step 202 and to be belonged to same people, thinks that then target people face is authentic and valid, otherwise is false people's face.
If it is multi-modal that the described pair of identifier's face method for anti-counterfeit is based on, then step 1 further comprises:
Have only target people face simultaneously by step 101 and 102, just be considered to detect by the live body of step 1.
The quotient images algorithm comprises the steps: mutually
Step 1021, gather a large amount of true man people's faces and the false people's face multispectral imaging composing training data set under different distance, carry out being divided by of Pixel-level for the image under any two different spectrum of same individual, form mutual quotient images group, suppose to select arbitrarily two spectrum lambda
1, λ
2, the image of same individual face under two spectrum is
With
Its mutual quotient images is defined as follows:
Wherein, ρ represents the reflectivity of people's face, and κ represents light source in the intensity of people's face surface, and z representative face is apart from the distance of light source, and (x y) represents coordinate on the facial image;
Step 1022 for all mutual quotient images, is divided into a plurality of overlapping or nonoverlapping fritters at a plurality of yardsticks, extracts the proper vector of each fritter, the proper vector of all fritters is made up, as the proper vector of the overall situation;
Step 1023, based on statistical learning method, training classifier on training dataset is used for distinguishing true, false people's face.
Step 203 if in face recognition application, if the target facial image facial image corresponding with recognition result, is regarded as by the sorter in the step 202 and to be belonged to same people, thinks that then target people face is authentic and valid, otherwise is false people's face;
If in people's face checking is used, if the target facial image facial image corresponding with nominator's identity of claiming, regarded as by the sorter in the step 202 and to belong to same people, think that then target people face is authentic and valid, otherwise be false people's face.
Every kind of different imaging type is called as a mode, and imaging type comprises visual light imaging, near infrared imaging, near ultraviolet imaging, thermal infrared imaging or ultrasonic imaging.
A kind of pair of identifier's face false proof device, this device comprises:
Sensing unit for use near infrared, ultrasound wave, RF-wise or visible image capturing head one or more, by the mode of real-time monitoring, is responded to the existence of people's face;
Multi-modal generation source comprises active light source under a plurality of spectrum, is used for one or more of the required 3D structured light of 3D imaging or ultrasonic generator;
Multi-modal data acquisition equipment be used for to be gathered the multispectral imaging of people's face, the thermal infrared photoimaging that human body itself sends, the 3D rendering of people's face or in the ultrasonic imaging one or more;
Multi-modal people's face detecting unit for detection of the people's face position in the multi-modality images, and sends to the anti-dummy unit of multi-modal pair of identifier's face with detected facial image;
The multi-modal pair of anti-dummy unit of identifier's face is used for whether checking target people face is authentic and valid people's face;
Display unit is used for showing the false proof result of people's face,
Wherein, the multi-modal pair of anti-dummy unit of identifier's face further comprises: multi-modal people's face live body detecting unit is used for that target people face is carried out live body and detects; Multi-modal people's face authentication unit is used for target people face is carried out authentication.
When described multi-modal people's face live body detecting unit carries out the live body detection to target people face, at first, the rough biologically active of judging target people face is wherein judged according in the following mode one or more: judge by thermal infrared to judge whether the temperature of target people face near 37 degree; By the depth information of 3D rendering judgement people face, judge whether face is the 3D object; By the ultrasonic reflections rate of ultrasonic reflections evaluating objects people face, judge whether the ultrasonic reflections rate of skin is similar to real human face; By the reflectivity of multispectral imaging evaluating objects people face under different spectrum, whether the multispectral reflectivity of judging skin is similar to real human face, if judge that by above-mentioned one or more modes the information index of target people face is similar to real human face, then continue accurately to judge the biologically active of target people face, with the multispectral facial image that collects, utilize mutual quotient images algorithm to carry out live body judgement accurately.
The quotient images algorithm comprises the steps: mutually
Gather a large amount of true man people's faces and the false people's face multispectral imaging composing training data set under different distance, carry out being divided by of Pixel-level for the image under any two different spectrum of same individual, form mutual quotient images group, suppose to select arbitrarily two spectrum lambda
1, λ
2, the image of same individual face under two spectrum is
With
Its mutual quotient images is defined as follows:
Wherein, ρ represents the reflectivity of people's face, and κ represents light source in the intensity of people's face surface, and z representative face is apart from the distance of light source, and (x y) represents coordinate on the facial image;
For all mutual quotient images, be divided into a plurality of overlapping or nonoverlapping fritters at a plurality of yardsticks, extract the proper vector of each fritter, the proper vector of all fritters is made up, as the proper vector of the overall situation;
Based on statistical learning method, training classifier on training dataset is used for distinguishing true, false people's face.
When multi-modal people's face authentication unit carries out authentication to target people face, at first gather the multi-modality images of a large amount of real human face, every image is extracted its textural characteristics; Secondly, the proper vector of image is subtracted each other in twos, whether belong to same individual according to two images, proper vector after will subtracting each other is divided in the class, two classes between class, utilize two class sorters of machine learning algorithm training, the sorter that training obtains can judge whether two proper vectors of input belong to same individual; If in face recognition application, if the target facial image facial image corresponding with recognition result, regarded as by above-mentioned two class sorters and to belong to same people, think that then target people face is authentic and valid, otherwise be false people's face; If in people's face checking is used, if the target facial image facial image corresponding with nominator's identity of claiming, regarded as by above-mentioned two class sorters and to belong to same people, think that then target people face is authentic and valid, otherwise be false people's face.
Every kind of different imaging type is called as a mode, and imaging type comprises visual light imaging, near infrared imaging, near ultraviolet imaging, thermal infrared imaging or ultrasonic imaging.
Advantage of the present invention: by the combination of live body detection with authentication, provide accurate, reliable people's face anti-counterfeiting detection result.
Description of drawings
The two identifier's face method for anti-counterfeit process flow diagrams under visible light that Fig. 1 proposes for the present invention;
Fig. 2 is put the fake method process flow diagram for the two identifier's faces under multi-modal that the present invention proposes;
The two identifier's face false proof device structured flowcharts under multi-modal that Fig. 3 proposes for the present invention;
The workflow diagram of the two identifier's face false proof devices under multi-modal that Fig. 4 proposes for the present invention;
The light source coverage synoptic diagram of the two identifier's face false proof devices under multi-modal that Fig. 5 proposes for the present invention;
In two identifier's face false proof device one examples that Fig. 6 proposes for the present invention human face region gradation of image average and people's face apart from harvester apart between concern synoptic diagram;
Fig. 7 is the people's face reflectance curve synoptic diagram of Black people and white man in certain spectral range;
Fig. 8 is the reflectance curve synoptic diagram of several frequently seen fraud people face in certain spectral range;
Fig. 9 is the panel synoptic diagram of multi-modal harvester in two identifier's face false proof device one examples of the present invention's proposition;
Figure 10 is people's face imaging synoptic diagram of three kinds of different spectrum, from left to right is followed successively by: visible light, 850nm near infrared light and 400nm purple light;
Figure 11 face thermal infrared imaging synoptic diagram of behaving;
Figure 12 face 3D imaging synoptic diagram of behaving;
Figure 13 is that ultrasound wave is at people's reflection wave synoptic diagram on the face.
Embodiment
The invention will be further described below in conjunction with concrete drawings and Examples.
The ultimate principle of people's face method for anti-counterfeit that the present invention proposes is based on the thought of two checkings.So-called two identifier's face is false proof, comprises following two steps: step 1, input people face is carried out the judgement of live body, non-living body, and this step and people's identity is irrelevant, and namely the nominator is irrelevant; Step 2 is carried out the authentication of people's face to the input facial image, has only when importing facial image and corresponding identity and be complementary, and just regards as authentic and valid people's face, and this step is relevant with the nominator.Have only simultaneously that the input people faces of judging by above two steps just are considered to effectively, people's face really.
What use in the step 1 is people's face live body detection technique, namely people's face is carried out live body, non-living body judgement, differentiates whether be true man's living body faces; Step 2 is actually the checking of people's face being carried out nominator, non-nominator.Wherein in step 2, if do face recognition application, then recognition result is the corresponding identity of target people face, has only both similarities greater than certain threshold value, and just by this step card, threshold value can be set up on their own according to the actual requirements by managerial personnel; The face checking is used if conduct oneself, and then the corresponding identity of target people face is the identity that target people face is claimed, the input facial image must just be thought by the authentication of people's face greater than certain threshold value with the similarity between the corresponding identity facial image.Step 2 is the classification to nominator and non-nominator's image.By the information of fusion steps 1 and step 2, reach false proof purpose reliably.
Why method of the present invention adopts above-mentioned steps 1 and step 2 simultaneously, is that potential false people's face type can't be estimated because in actual applications, and simple live body detects can't keep high-accuracy always.And on the other hand, even false people's face has passed through the live body detection, also have reason to believe to have certain difference between this falseness people's face and counterfeit everybody face of appointment, therefore can further strengthen the false proof precision of people's face by extracting, differentiating this difference.Therefore the facial image that proposes input needs to detect and the authentication of people's face by people's face live body simultaneously, just can regard as authentic and valid people's face.
Traditional false proof research of people's face also rests on people's face live body and detects, and has ignored the checking to input people face.In fact, people's face live body of step 1 detects, and is irrelevant with the nominator; People's face authentication of step 2 then is the authentication at the nominator.People's face anti-counterfeiting technology of the present invention detects in conjunction with the authentication of people's face and live body, can effectively improve the false proof reliability of people's face.
In of the present invention pair of identifier's face method for anti-counterfeit, concrete application form under visible light and the concrete application form under multi-modal have been proposed further.
Two identifier's face method for anti-counterfeit under the visible light are applicable to traditional visible light recognition of face, people's face verification system, need not additional hardware and can finish the false proof task of people's face.False facial image can be regarded as the real human face image at the image through obtaining after certain aftertreatment, and therefore comparing its picture quality of true picture will have certain loss.By the target people's face that captures is extracted polytype texture information, can fully excavate the appearance features of target people face on the dermatoglyph details, and then further classify according to pre-set evaluation criteria.In addition, also can further strengthen its accuracy by introducing traditional man-machine interaction process.
For the present invention propose multi-modal under two identifier's face method for anti-counterfeit, then further adopt multiple modalities fully to excavate people's face essential characteristic.Existing recognition of face, people's face verification technique also only rest on utilizes a kind of mode (for example visible light or near infrared) to obtain facial image.Therefore we think that this data acquisition modes can not fully excavate the skin properties of people's face, can not reach higher anti-spurious accuracy, propose and designed two identifier's face false proof devices under multi-modal.This device comprises multispectral imaging device under the different spectrum, thermal infrared imaging device, ultrasonic imaging apparatus etc., fully excavates the essential physical characteristics of people's face skin from different aspects.By anatomizing true man people's face and the characteristic of typical false people's face under different modalities, choose the combination of suitable mode, the feature of tool resolving ability is provided for the follow-up false proof algorithm of people's face.
Two identifier's face method for anti-counterfeit under the visible light that the present invention proposes can by to the analysis of dermatoglyph details and/or the motion of people's face, accurately be judged the true and false of target people face on the basis that does not rely on additional hardware.And the present invention proposes based on multi-modal two identifier's face method for anti-counterfeit, with respect to existing people's face live body detection algorithm, not only can defend more attack type, and have few, characteristics such as the user experiences well, accuracy rate height of time spent.Can provide abundanter people's face information by multi-modal people's face information of obtaining, fully excavate the essential characteristic of people's face, increase the discrimination of true man people's face and false people's face, can effectively solve the false proof difficult problem of people's face.
The concrete applicating flow chart of two identifier's face method for anti-counterfeit under visible light that Fig. 1 proposes for the present invention.With reference to Fig. 1, detect in the step 101 at live body, the people's face live body that uses dermatoglyph to combine with facial movement detects strategy.In authentication step 102, the corresponding identity of target people face (checking of people's face is the identity of claiming in using, and is the identity of recognition result correspondence in the face recognition application) is verified, if matching similarity is greater than certain threshold value, then think real human face, otherwise be false people's face.Having only target people face to pass through 101 and 102 liang of step ability assertive goal people faces simultaneously is real human face.
Live body detects step 101 and further comprises step 1011 and step 1012: step 1011, at first target people face is extracted various textural characteristics, for example LBP (Local Binary Pattern), HOG (Histograms of Oriented Gradients) feature etc. is trained the liveness detector that obtains based on dermatoglyph by gathering true false people's face sample then by machine learning algorithm (as support vector machine SVM).If judge it is real human face, enter step 1012.
An example of step 1011 is to utilize the LBP descriptor of different scale, for example
The target facial image is carried out filtering, then image is carried out multiple dimensioned division, for example be divided into 1 * 1,3 * 3,5 * 5 fritter at the histogram of three kinds of LBP descriptors of each piece the inside statistics, is linked at together textural characteristics as target people face to all histograms.
Gather the image of true, false people's face in a large number then, for example, gather 50 people's real human face image, utilize its facial image to be made into the photo of different sizes then, and then gather photograph image.Take out human face region, extract feature according to the operation of previous step.Utilize the training of SVM algorithm to obtain a sorter then.
In step 1012, utilize man-machine interaction further to detect the biologically active of target people face.For example, can give the instruction of selling user's nictation or shaking the head by face identification system.Whether made corresponding actions by detecting target people face, thereby judged whether target people face is real human face.In this step, can utilize estimation or template matching algorithm to carry out facial movement and estimate.For example, if adopt the form of nictation, can utilize optical flow method to calculate the motion vector of target people face eye areas, and then judge whether to have taken place action nictation.Perhaps template matching algorithm, what a sorter of opening eyes, closing one's eyes of training in advance carries out motion detection then.
The mutual example of one personal-machine is that face identification system provides the command request user within a certain period of time, for example 5 seconds, blinks.By the human eye state sorter that trains, detect and whether to have occurred in the period at this section opening eyes-to close one's eyes-process of opening eyes.If occur, then think real human face, otherwise then think false people's face, enter step 1021.Wherein human eye state sorter above-mentioned can be collected in a large number the image of opening eyes, close one's eyes in advance, utilizes the training of svm classifier device to obtain the sorter of eye state then, is used for above-mentioned blink detection.
In authentication step 102, facial image in the database (is for example extracted feature, LBP and Gabor feature), then the proper vector of the face images of gathering is subtracted each other in twos, whether belong to same individual according to two images, proper vector after will subtracting each other is divided in the class, two classes between class, utilizes two class sorters of machine learning algorithm training, and the sorter that obtains of training can judge whether two proper vectors of input belong to same individual thus;
Through above step, the similarity that belongs between same people's the face characteristic should be greater than the similarity between the face characteristic of different people.By setting a rational threshold value, can be used for authentication: if the similarity between step 102 target people's face and its identity of claiming is then thought and passed through authentication greater than threshold value; Otherwise failure.
Fig. 2 is the two applicating flow charts of identifier's face method for anti-counterfeit under multi-modal form.This method adopts multi-modal as carrier, gather multi-modal facial image, utilize abundant information that multi-modality images provides and utilize different biological features to have the characteristics of different physical characteristicss, by multi-modal information fusion, designed rationally, reliably two false proof algorithms of identifier's face.
Two identifier's face method for anti-counterfeit under multi-modal form comprise live body verification step 201 and authentication step 202 liang step.
In biological information checking 201, adopt by slightly going on foot strategy to two of essence.
At first in the first step 2011, utilize multi-modal people's face information of obtaining, the live body characteristic of input people face is judged roughly.An example is: at first carry out temperature detection by thermal infrared images, if meet the temperature range (whether being 37 degree for example) of real human body, then carry out the judgement of people's face depth information by the 3D facial image, if judge that input people face is a three-dimensional body, then continue to utilize the ultrasonic reflections rate of ultrasonic reflections wave measurement input people face, if reflectivity and true man's human face similarity, whether in the reasonable scope then check its multispectral the average image brightness, if rationally, then be judged as true man people's face, otherwise be false people's face.In this step, can choose people's face live body characteristic of judging as rough according to specific people's face modal dynamic.
After the first step 2011 was regarded as true man people's face, in second step 2012, at the multi-modality imaging of people's face, the present invention proposed the people's face live body detection algorithm based on mutual quotient images, provides more accurate, meticulous testing result; If the quotient images algorithm judges that this person's face is true man people's face mutually, then people's face biologically active is imported in explanation.If be judged as true man people's face in second step 2012, then be true man people's face, otherwise be false people's face.
In step 2012, utilize mutual quotient images algorithm to carry out accurate people's face live body and detect.Mutually quotient images refer to image under any two spectrum carry out the relevant position pixel value do the resulting image of division (Mutual Quotient Image, MQI).Quotient images can reflect the relation of people's face between two wave band reflectivity of taking mutually, and irrelevant with the shape of people's face.According to the definition of mutual quotient images, suppose to select arbitrarily two spectrum lambda
1, λ
2, the image of same individual face under two spectrum is
With
Its mutual quotient images is defined as follows:
Wherein, ρ represents the reflectivity of people's face, and κ represents light source in the intensity of people's face surface, and z representative face is from the distance between the light source, and (x y) represents coordinate on the facial image.
If guarantee λ
1, λ
2The light source luminescent power unanimity of two spectrum, then in suitable distance range, λ
1, λ
2It is 1 that the ratio of the intensity of two kinds of light sources waits approximately, and therefore (4) formula can approximate
As can be seen, this moment, mutual quotient images reflected that people's face is at λ
1, λ
2Therefore the ratio of the reflectivity under two kinds of spectrum is a feature that can reflect people's face intrinsic propesties, can be used for designing the live body detection algorithm.
In the derivation of formula (5), suppose in suitable distance range λ
1, λ
2It is 1 that the ratio of the intensity of two kinds of light sources waits approximately.By the appropriate design light source, can satisfy this hypothesis in practice.For example, the present invention has gathered 480nm and 850nm two kinds of light sources under the consistent situation of luminous power, at distance light source 40cm between the 90cm, the situation of change of same individual's facial image gray average, as shown in Figure 6, as can be seen, to wait approximately be that 1 hypothesis is rational to the ratio of the intensity of two kinds of light sources.
In multi-modal people's face live body detection algorithm, after having obtained the mutual quotient images of any two spectrum, can feature reasonable in design, detect in order to carry out live body.Proper vector is extracted can adopt several different methods, as: intensity histogram, Gabor wave filter etc., likelihood ratio (Likelihood Ratio) etc.After selected characteristic type, can carry out piecemeal to mutual quotient images, and do multiple dimensioned processing, obtain on the different scale, the mutual quotient images proper vector of people's face of diverse location, true, the dummy's face sample of a large amount of collections utilizes the Boosting algorithm to carry out the training that live body detects sorter then.
In multi-modal people's face live body detection algorithm, should take into full account difference in reflectivity true, fraud people face, carry out light source and select.Fig. 7 illustration people's face reflectance curve of multispectral down Black people and white man.Fig. 8 illustration the reflectance curve of multispectral down several frequently seen fraud people face, comprise two kinds of different silica gel and photo.According to this two width of cloth curve, can select to provide foundation for the spectrum in multi-modal people's face live body detection.
Idiographic flow based on people's face live body detection algorithm of mutual quotient images is as follows:
(1), gather a large amount of true man people's faces and the reflection intensity data composing training data set of fraud people face under different distance, carry out MQI for the image under any two different spectrum of same individual and calculate.
(2), on all MQI images, be divided into a plurality of fritters (overlapping or not overlapping) at a plurality of yardsticks, extract the proper vector of each fritter, the proper vector of all fritters is made up, as the proper vector of the overall situation.
(3), based on statistical learning method, training classifier on training dataset is as SVM (support vector machine), LDA (linear discriminant analysis), Boosting etc.
Below by giving an example to further specify the mutual quotient images algorithm that live body detects step 2012.
For example, adopt two kinds of light sources of 480nm and 940nm to carry out imaging, the facial image of acquisition is respectively I
480, I
940Stipulate I then
480Be reference picture, the mutual quotient images that calculates under these two wave bands is MQI
940,480(x, y)=I
940(x, y)/I
480(x, y).The present invention has only provided the situation of two kinds of wave bands by way of example at this, also can select the light source of any multiple wave band according to actual conditions.
128 * 128 MQI image carries out multiple dimensioned processing through after the pre-service, is divided into 5 yardsticks, and its size is respectively 128 * 128 pixels, 64 * 64 pixels, 32 * 32 pixels, 16 * 16 pixels, 8 * 8 pixels.Based on the probability model that on training set, obtains by statistical learning, for the every bit on the mutual quotient images, can calculate the likelihood that it belongs to live body and non-living body
Wherein the G representative image is from live body,
Representative image is from non-living body, and (x y) is image coordinate.These two amounts are divided by, can obtain the local likelihood ratio of mutual quotient images:
For the mutual quotient images of above-mentioned multiresolution, all local likelihood ratios can constitute a live body proper vector, and its dimension is 21824.
In order to make feature have more discrimination and have higher operation efficiency, the live body feature extraction algorithm utilizes Boosting to carry out feature selecting, selects 3000 dimensional features of tool resolving ability from original high-dimensional feature.
Gather a large amount of true, dummy's face samples then, set up tranining database, feature label after selecting according to above-mentioned Boosting carries out feature extraction, and utilize support vector machine (Support Vector Machine, SVM) methodology acquistion to one two a class sorter are used for the proper vector of input is carried out the judgement of live body, non-living body.
In authentication step 202, need carry out the similarity checking to input people face and its corresponding identity.Concrete verification algorithm and the verification method under the visible light 102 are similar, and difference is the summation that is characterized as all features on the multi-modality images imported.
Have only the biological information of working as checking and two steps of authentication to assert that all input people face is true man people's face, input people face is just calculated by the false proof judgement of people's face.
Below by people's face verification algorithm of giving an example to further specify in the authentication step 202, wherein be applied as example with the checking of people's face.
Suppose that everyone has N to open the image of different modalities, at first every facial image is carried out LBP feature and Gabor feature extraction, form the proper vector f of this image
k, k=1:N.
The proper vector that will belong to every image in the same multi-modality images combination then is concatenated into forms unified proper vector F=[f
1...; f
N], then F is everyone multi-modal proper vector.
In the training process of people's face checking sorter, positive sample is multispectral proper vector F poor that belongs to same individual, and negative sample is poor for the multispectral proper vector F's that do not belong to same individual.Utilize the Boosting algorithm to carry out feature and select, obtain a character subset.
Everyone multi-modality images that training data is concentrated, the sample of selecting according to Boosting carries out feature extraction, and utilizes the LDA algorithm to carry out discriminatory analysis.
Through above step, the similarity that belongs between same people's the face characteristic should be greater than the face characteristic similarity between the different people.If the similarity in step 202 between target people face and its identity of claiming is then thought and has been passed through authentication greater than threshold value; Otherwise failure.
The invention allows for a kind of pair of identifier's face false proof device.Fig. 3 is the structured flowchart that the present invention is based on multi-modal two identifier's face false proof devices.Fig. 4 is the workflow diagram based on multi-modal two identifier's face false proof devices of the present invention.
Of the present invention based on multi-modal two identifier's face false proof devices in, one or more in the mode such as that wherein multi-modal comprises is multispectral, 3D, ultrasound wave.Because people's face skin has different reflectivity under different spectrum, therefore the present invention introduces multispectral people's face imaging system, be used for to gather, the imaging of analyst's face under different spectrum, the intrinsic propesties that fully excavates people's face, thus abundant face characteristic is provided for follow-up people's face is false proof.Choosing of spectrum can comprise near infrared light, mid-infrared light, far infrared (thermal infrared), black light etc., to reflect the different reflection characteristics of people's face as far as possible.Especially, thermal infrared images refers to the infrared light imaging that the human body self heat gives out, and is relevant with individual's physique, biological properties, and the remarkable individual difference of tool is suitable as the false proof foundation of people's face.Above light source all needs multispectral acquisition system that initiatively light source is provided except Thermal Infra-Red.
The present invention introduces the 3D facial image simultaneously, and ultrasonic imaging, with multispectral image, has constituted multi-modal facial image jointly and has obtained system.The depth information of the people face position that obtains by 3D rendering is the false proof important evidence of people's face, can resist the attack of common false people's face, for example photo, video etc.The method of ultrasonic imaging by measuring people's face skin for hyperacoustic reflectivity, can provide the physical characteristics tolerance means of another people's face skin, further the demand of auxiliary people's face live body detection.
In conjunction with Fig. 3 and Fig. 4, of the present inventionly comprise sensing unit 301 based on multi-modal two identifier's face false proof devices, multi-modal generation source 302, multi-modal data acquisition equipment 303, multi-modal people's face detecting unit 304, the multi-modal pair of anti-dummy unit 305 of identifier's face (comprise multi-modal people's face live body detecting unit 3051, multi-modal people's face identity authenticating unit 3052), control module 306 and display unit 307.
In one example, sensing unit 301 is the visible image capturing head, and its mode with monitoring is carried out the induction of people's face.A visible image capturing circle collection image also detects whether there is people's face.If there is no people's face then continues to gather visible images and carries out the detection of people's face; If there is people's face, then waited for for 0.5 second and gather image again and detect people's face.If this moment, people's face also existed, then explanation has stable, effective people's face to occur, and sends a signal to control module 306 then, begins corresponding image acquisition work; If people's face disappears after waiting for, illustrate that this person's face probably is not the people's face that carries out the multi-modality images collection, think noise and ignore.Continue to gather visible images and detect whether there is people's face.
For multispectral light source wherein, the light that light source sends should meet two principles: 1, in the suitable distance scope, in plane, 302 dead ahead, multi-modal generation source, certain area planted agent keeps light intensity roughly even.As shown in Figure 5, locate in collecting device dead ahead certain distance (d), (circular shown in the figure) light intensity should keep even in certain area.2, luminous intensity should keep in the reasonable scope, makes imaging device can collect facial image clearly, and it is too big and cause that the user's is uncomfortable to be unlikely to light intensity again.
Multi-modal data acquisition equipment 303 is used for gathering the active light source and is radiated at the multispectral light that the people is reflected on the face then, also is used for gathering the thermal infrared light that human body itself sends, the 3D rendering of people's face, and the ultrasonic imaging of people's face in addition.This collecting device is including, but not limited to following one or more units: respond the camera of each source light, the receiving tube that responds each spectrum light or photodiode, thermal infrared induction camera or inductor, 3D rendering collecting device, supersonic imaging device or receiver.
Multi-modal data acquisition unit 303 comprises that at first imaging device corresponding to each spectrum in 302 in order to gather the multispectral light of people's face reflection, comprises imaging device and corresponding filter disc, comprises thermal infrared, 3D, supersonic imaging device or inductor in addition.The camera of the preferred good response multispectral light source of multispectral imaging equipment light, this moment, the return data type was image.If condition is limited, also can use other receiving equipment, for example respond the receiving tube, photodiode of multispectral light etc., this moment, the return data type was the reflection strength scalar.A kind of light source in the multispectral light source can corresponding camera, also can utilize single camera to respond the multispectral light source of a plurality of wave bands.Camera should have higher sensitivity at the spectrum place that responds.For supersonic imaging device, should be consistent with the ultrasonic generator holding frequency in 302; If condition does not allow, also can select ultrasonic receiver for use.For thermal infrared, preferred thermal infrared camera, also can select for use can temperature sensor inductor.For the 3D camera, what then collect is the image of reflection people face depth information.
In the multispectral imaging equipment of multi-modal data acquisition unit 303, need to be equipped with the filter disc of corresponding wave band, in order to eliminate surround lighting and other wave band light to the interference of this wave band.Filter disc should be placed on the imaging device front of corresponding wave band, and is close to cam lens or receiving equipment, enters to prevent veiling glare.
The multi-modal people's face of sensing unit detecting unit 304, be used for the facial image of multi-modality images imaging device collection is carried out pre-service, then to detecting through pretreated facial image, all be detected when face images and think the detected people's of being face under the situation of people's face and eyes.
Multi-modal two identifier's faces are prevented dummy unit 305, comprise that multi-modal people's face live body detects 3051 and verifies 3,052 two subelements with multi-modal people's face.Wherein detect in 3051 at multi-modal people's face live body, what employing was above mentioned designs suitable multi-modal people's face live body sorter by slightly going on foot strategy to two of essence; Multi-modal people's face identity authenticating unit 3052 is extracted from multi-modal facial image and can be determined that the information of target identities carries out the authentication of people's face.Wherein, multi-modal people's face live body detecting unit 3051 and multi-modal people's face identity authenticating unit 3052 have been formed the realization unit 305 based on the multi-modal false proof algorithm of two identifier's faces of the present invention jointly.
With reference to Fig. 3 and Fig. 4, the control mode of control module 306 is: after there is signal in the people's face that receives sensing unit 301 transmissions, at first provide control signal, open the light source of spectrum 1, wait for that then the regular hour gives the camera exposure, gather the picture signal corresponding to the camera of spectrum 1 then, close the light source of spectrum 1 then.Provide signal then, open the light source of spectrum 2, wait for certain time shutter, hold and gather picture signal corresponding to the camera of spectrum 2, close the light source of spectrum 2 then, and the like, finish up to the image data acquiring of all spectrum.If do not use camera under the some spectrum, and be to use other receiving equipment, as receiving tube, photodiode etc., then read corresponding receiving intensity numerical value.Control the thermal infrared camera then and carry out image acquisition.After thermal infrared, control 3D video camera carries out the 3D man face image acquiring.Control ultrasound wave emission ultrasound wave then, and carry out imaging with supersonic imaging device.
An example is: control module 306 is made up of host computer PC end software.Control module 306 at first provides the open command of light source 1 after the signal that receives sensing unit 301 transmissions, wait for that 50ms provides the acquisition of the camera (or receiving tube) of corresponding light source 1 then, by camera (or receiving tube) image data.Make light source 1 extinguish then, provide the open command of light source 2, wait for 50ms, make the camera (or receiving tube) of light source 2 carry out data acquisition.And the like, till the camera of all light sources all collects data.When gathering thermal infrared and 3D rendering then, do not need this moment to wait for and directly to gather.Open ultrasonic transmitter then, and by supersonic imaging device echo is received and imaging.Then control module 306 can with each camera collection to view data send into multi-modal people's face detecting unit 304.
The facial image that display unit 307 is gathered by multi-modal data acquisition unit 303 in order to demonstration, and provide various intermediate results or feedback information, make things convenient for man-machine interaction.
It should be noted that if certain above-mentioned mode does not have corresponding image data acquiring equipment, also can replace with other non-image formula induction instrument aratus.
Fig. 9 has provided the synoptic diagram of multi-modal generation source and multi-modal data acquisition unit by way of example.Wherein, multi-modality images harvester panel 804 plays the effect of device frame.Panel is divided into two parts up and down, the first half is multi-modal generation source 901 and multi-modal data acquisition unit 902, and the latter half is display unit 905, is made up of a lcd screen, be a visible image capturing 903 between two parts, use as sensing unit.In the first half, three multi-modal emissive sources are respectively 800nm multispectral light source, 3D structure light source and ultrasound wave emissive source.Three kinds of emissive source cross arrangements, and form rectangle, can guarantee that like this each emissive source can form even distribution in the certain limit of device the place ahead.Central authorities are four imaging devices (or receiving equipment) launching in a steady stream, comprise multispectral imaging equipment (camera the place ahead all covers the filter disc of corresponding wave band, to prevent the interference of visible light or other spectrum light), thermal infrared camera (being used for gathering thermal infrared images), 3D and supersonic imaging device.When testing, people's face should be positive in the face of this harvester.Control module is not comprised on the face version of multispectral harvester, but an independent one-tenth part (can be single-chip microcomputer, also can be upper computer software) is connected by control signal wire with multispectral harvester panel.
Multi-modal people's face detecting unit, the multi-modal pair of anti-dummy unit of identifier's face are the application program of host computer, after receiving the multi-modal facial image that collects, deliver to above two unit respectively, and provide corresponding result.
Above-mentioned workflow based on multi-modal two identifier's face false proof devices as shown in Figure 4.With reference to Fig. 4, at first responded to the existence of people's faces by sensing unit 401; If there is no people's face then continue cycle detection, and in fact, sensing unit 401 can not be judged the detected people's of being face, just when having sensed the object existence, namely thinks to sense people's face; If there is people's face, then give an order to control module 402, send control command by control module 402, instruct multi-modal generation source 403 to open, close, and multi-modal data acquisition unit 404 image data; Enter multi-modal people's face detecting unit 405 then and carry out the detection of people's face, if do not detect people's face in the image that has, then signal to display unit 407.Output detects the information of failure, and returns sensing unit 401, carries out image acquisition again; If all mode images all detect people's face, then enter the anti-dummy unit 406 of multi-modal pair of identifier's face, subsequent signal is given display unit 407, so that output people face detects certain facial image of information or display capture; Carry out people's face live body detection judgement 4061 and people's face authentication 4062 after entering the anti-dummy unit 406 of multi-modal pair of identifier's face, if for fraud people face then provide the information that corresponding live body detects failure by display unit 407, and return sensing unit 401, carry out the image acquisition of a new round; If also provided by display unit 407 for true man people's face, wait for a period of time then, return sensing unit 401, people's face of a beginning new round detects.
In one example, multi-modal people's face detecting unit 405 is the application program of host computer PC end, and the corresponding people's face of every image call that is used for that multi-modal data collector 403 is collected detects sorter and carries out the detection of people's face.If all images all detects people's face, carry certain facial image to be used for (for example showing for display unit 407, select the facial image under the visible light for use), and the facial image under detected all spectrum inputed to the anti-dummy unit 406 of multi-modal pair of identifier's face.If all do not detect people's face, then carry the result who detects failure to show for display unit 407, and return sensing unit 401, restart image sensing.
At last, the present invention must point out, the two identifier's face method for anti-counterfeit and the device thereof that utilize the present invention to propose, and the user can be applicable to different biological mode, for example people's face, iris etc. according to the needs of oneself.And can freely select the mode combination according to actual conditions, for example, can select different spectral combination separately for use, also can unite use in conjunction with thermal infrared light, 3D rendering or ultrasonic imaging.
Above-described specific embodiment; purpose of the present invention, technical scheme and beneficial effect are further described; institute is understood that; the above only is specific embodiments of the invention; be not limited to the present invention; within the spirit and principles in the present invention all, any modification of making, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. two identifier's face method for anti-counterfeit is characterized in that described method comprises:
Step 1 is carried out live body to target people's face of gathering and is detected, and judges whether biologically active of target people face, if target people face is had the live body characteristic by identification, then changes step 2 over to;
Step 2 if in face recognition application, is then calculated the similarity between the target people's face collect people's face corresponding with recognition result, as if greater than a certain threshold value, thinks that then this target people face is authentic and valid people's face;
If in people's face checking is used, then calculate the similarity between the corresponding people's face of identity that target people's face of collecting and target people face claim, as if greater than a certain threshold value, think that then this target people face is authentic and valid people's face,
Wherein step 1 is irrelevant with the nominator, and step 2 is relevant with the nominator, and after the checking by step 1 and step 2 simultaneously of target people face, just can be identified as is authentic and valid people's face, is false people's face otherwise be identified as;
If it is multi-modal that the described pair of identifier's face method for anti-counterfeit is based on, then step 1 further comprises:
Step 101 is judged the biologically active of target people face roughly, wherein judges according in the following mode one or more: judge by thermal infrared to judge whether the temperature of target people face near 37 degree; By the depth information of 3D rendering judgement people face, judge whether face is the 3D object; By the ultrasonic reflections rate of ultrasonic reflections evaluating objects people face, judge whether the ultrasonic reflections rate of skin is similar to real human face; By the reflectivity of multispectral imaging evaluating objects people face under different spectrum, whether the multispectral reflectivity of judging skin is similar to real human face, if judge that by above-mentioned one or more modes the information index of target people face is similar to real human face, then enter step 102;
Step 102 is accurately judged the biologically active of target people face, with the multispectral facial image that collects, utilizes mutual quotient images algorithm to carry out accurately live body and judges;
Have only target people face simultaneously by step 101 and 102, just be considered to detect by the live body of step 1.
2. according to claim 1 pair of identifier's face method for anti-counterfeit is characterized in that, if described pair of identifier's face method for anti-counterfeit is based on visible light, then step 1 further comprises:
Step 101, target people face is carried out live body to be detected, at first gather true, false people's face sample in a large number, target people face is extracted various textural characteristics, training living body detects texture classifier, if target people face is detected texture classifier by live body and regards as real human face, then enter step 2, otherwise regard as false people's face;
Step 102, determine the validity of target people face by man-machine interaction, wherein system sends instruction, require the user to make certain action, system constantly detects target people face and whether makes corresponding actions then, if detect the generation of above-mentioned action within a certain period of time, judge that then target people face is real human face, otherwise be false people's face;
Have only target people face simultaneously by step 101 and 102, just be considered to detect by the live body of step 1.
3. according to claim 2 pair of identifier's face method for anti-counterfeit is characterized in that step 2 further comprises:
Step 201 is at first gathered a large amount of real human face images, and every facial image is extracted its textural characteristics;
Step 202, then the proper vector of the face images of gathering is subtracted each other in twos, whether belong to same individual according to two images, proper vector after will subtracting each other is divided in the class, two classes between class, utilize two class sorters of machine learning algorithm training, train the sorter that obtains to judge whether two proper vectors of input belong to same individual thus;
Step 203 if in face recognition application, if the target facial image facial image corresponding with recognition result, is regarded as by the sorter in the step 202 and to be belonged to same people, thinks that then target people face is authentic and valid, otherwise is false people's face;
If in people's face checking is used, the target facial image facial image corresponding with nominator's identity of claiming then regarded as by the sorter in the step 202 and to be belonged to same people, thinks that then target people face is authentic and valid, otherwise is false people's face.
4. according to claim 1 pair of identifier's face method for anti-counterfeit is characterized in that, the quotient images algorithm comprises the steps: mutually
Step 1021, gather a large amount of true man people's faces and the false people's face multispectral imaging composing training data set under different distance, carry out being divided by of Pixel-level for the image under any two different spectrum of same individual, form mutual quotient images group, suppose to select arbitrarily two spectrum lambda
1, λ
2, the image of same individual face under two spectrum is
With
Its mutual quotient images is defined as follows:
Wherein, ρ represents the reflectivity of people's face, and κ represents light source in the intensity of people's face surface, and z representative face is apart from the distance of light source, and (x y) represents coordinate on the facial image;
Step 1022 for all mutual quotient images, is divided into a plurality of overlapping or nonoverlapping fritters at a plurality of yardsticks, extracts the proper vector of each fritter, the proper vector of all fritters is made up, as the proper vector of the overall situation;
Step 1023, based on statistical learning method, training classifier on training dataset is used for distinguishing true, false people's face.
5. according to claim 1 pair of identifier's face method for anti-counterfeit is characterized in that step 2 further comprises:
Step 201 is gathered the multi-modality images of a large amount of real human face, and every image is extracted its textural characteristics;
Step 202, the proper vector of image is subtracted each other in twos, whether belong to same individual according to two images, proper vector after will subtracting each other is divided in the class, two classes between class, utilize two class sorters of machine learning algorithm training, the sorter that training obtains can judge whether two proper vectors of input belong to same individual;
Step 203 if in face recognition application, if the target facial image facial image corresponding with recognition result, is regarded as by the sorter in the step 202 and to be belonged to same people, thinks that then target people face is authentic and valid, otherwise is false people's face;
If in people's face checking is used, if the target facial image facial image corresponding with nominator's identity of claiming, regarded as by the sorter in the step 202 and to belong to same people, think that then target people face is authentic and valid, otherwise be false people's face.
6. according to claim 1 pair of identifier's face method for anti-counterfeit is characterized in that, every kind of different imaging type is called as a mode, and imaging type comprises visual light imaging, near infrared imaging, near ultraviolet imaging, thermal infrared imaging or ultrasonic imaging.
7. two identifier's face false proof device, this device comprises:
Sensing unit for use near infrared, ultrasound wave, RF-wise or visible image capturing head one or more, by the mode of real-time monitoring, is responded to the existence of people's face;
Multi-modal generation source comprises active light source under a plurality of spectrum, is used for one or more of the required 3D structured light of 3D imaging or ultrasonic generator;
Multi-modal data acquisition equipment be used for to be gathered the multispectral imaging of people's face, the thermal infrared photoimaging that human body itself sends, the 3D rendering of people's face or in the ultrasonic imaging one or more;
Multi-modal people's face detecting unit for detection of the people's face position in the multi-modality images, and sends to the anti-dummy unit of multi-modal pair of identifier's face with detected facial image;
The multi-modal pair of anti-dummy unit of identifier's face is used for whether checking target people face is authentic and valid people's face;
Display unit is used for showing the false proof result of people's face,
Wherein, the multi-modal pair of anti-dummy unit of identifier's face further comprises: multi-modal people's face live body detecting unit is used for that target people face is carried out live body and detects; Multi-modal people's face authentication unit is used for target people face is carried out authentication;
It is characterized in that, when described multi-modal people's face live body detecting unit carries out the live body detection to target people face, at first, the rough biologically active of judging target people face, wherein judge according in the following mode one or more: judge by thermal infrared to judge whether the temperature of target people face near 37 degree; By the depth information of 3D rendering judgement people face, judge whether face is the 3D object; By the ultrasonic reflections rate of ultrasonic reflections evaluating objects people face, judge whether the ultrasonic reflections rate of skin is similar to real human face; By the reflectivity of multispectral imaging evaluating objects people face under different spectrum, whether the multispectral reflectivity of judging skin is similar to real human face, if judge that by above-mentioned one or more modes the information index of target people face is similar to real human face, then continue accurately to judge the biologically active of target people face, with the multispectral facial image that collects, utilize mutual quotient images algorithm to carry out live body judgement accurately.
8. according to claim 7 pair of identifier's face false proof device is characterized in that, the quotient images algorithm comprises the steps: mutually
Gather a large amount of true man people's faces and the false people's face multispectral imaging composing training data set under different distance, carry out being divided by of Pixel-level for the image under any two different spectrum of same individual, form mutual quotient images group, suppose to select arbitrarily two spectrum lambda
1, λ
2, the image of same individual face under two spectrum is
With
Its mutual quotient images is defined as follows:
Wherein, ρ represents the reflectivity of people's face, and κ represents light source in the intensity of people's face surface, and z representative face is apart from the distance of light source, and (x y) represents coordinate on the facial image;
For all mutual quotient images, be divided into a plurality of overlapping or nonoverlapping fritters at a plurality of yardsticks, extract the proper vector of each fritter, the proper vector of all fritters is made up, as the proper vector of the overall situation;
Based on statistical learning method, training classifier on training dataset is used for distinguishing true, false people's face.
9. according to claim 7 pair of identifier's face false proof device is characterized in that, when multi-modal people's face authentication unit carries out authentication to target people face, at first gathers the multi-modality images of a large amount of real human face, and every image is extracted its textural characteristics; Secondly, the proper vector of image is subtracted each other in twos, whether belong to same individual according to two images, proper vector after will subtracting each other is divided in the class, two classes between class, utilize two class sorters of machine learning algorithm training, the sorter that training obtains can judge whether two proper vectors of input belong to same individual; If in face recognition application, if the target facial image facial image corresponding with recognition result, regarded as by above-mentioned two class sorters and to belong to same people, think that then target people face is authentic and valid, otherwise be false people's face; If in people's face checking is used, if the target facial image facial image corresponding with nominator's identity of claiming, regarded as by above-mentioned two class sorters and to belong to same people, think that then target people face is authentic and valid, otherwise be false people's face.
10. according to each described pair of identifier's face false proof device of claim 7-9, it is characterized in that: every kind of different imaging type is called as a mode, and imaging type comprises visual light imaging, near infrared imaging, near ultraviolet imaging, thermal infrared imaging or ultrasonic imaging.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012100594547A CN102622588B (en) | 2012-03-08 | 2012-03-08 | Dual-certification face anti-counterfeit method and device |
| PCT/CN2013/000228 WO2013131407A1 (en) | 2012-03-08 | 2013-03-05 | Double verification face anti-counterfeiting method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012100594547A CN102622588B (en) | 2012-03-08 | 2012-03-08 | Dual-certification face anti-counterfeit method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102622588A CN102622588A (en) | 2012-08-01 |
| CN102622588B true CN102622588B (en) | 2013-10-09 |
Family
ID=46562498
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2012100594547A Active CN102622588B (en) | 2012-03-08 | 2012-03-08 | Dual-certification face anti-counterfeit method and device |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN102622588B (en) |
| WO (1) | WO2013131407A1 (en) |
Families Citing this family (168)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102622588B (en) * | 2012-03-08 | 2013-10-09 | 无锡中科奥森科技有限公司 | Dual-certification face anti-counterfeit method and device |
| US9032510B2 (en) * | 2012-09-11 | 2015-05-12 | Sony Corporation | Gesture- and expression-based authentication |
| CN103036680A (en) * | 2012-12-10 | 2013-04-10 | 中国科学院计算机网络信息中心 | Realm name certification system and method based on biological feature recognition |
| CN103198294B (en) * | 2013-02-22 | 2016-05-11 | 广州市朗辰电子科技有限公司 | The recognition methods of a kind of video or image viewpoint type |
| CN104683302A (en) * | 2013-11-29 | 2015-06-03 | 国际商业机器公司 | Authentication method, authentication device, terminal equipment, authentication server and system |
| CN103634120A (en) * | 2013-12-18 | 2014-03-12 | 上海市数字证书认证中心有限公司 | Method and system for real-name authentication based on face recognition |
| CN103793690B (en) * | 2014-01-27 | 2017-08-18 | 天津科技大学 | A kind of human-body biological biopsy method detected based on subcutaneous haematic flow and application |
| US11188770B2 (en) | 2014-03-13 | 2021-11-30 | Nec Corporation | Detecting device, detecting method, and recording medium |
| CN103886301B (en) * | 2014-03-28 | 2017-01-18 | 北京中科奥森数据科技有限公司 | Human face living detection method |
| RU2690214C2 (en) | 2014-04-07 | 2019-05-31 | Айверифай Инк. | Bio-binding for user authentication |
| CN103984941B (en) * | 2014-06-10 | 2017-04-12 | 深圳市赛为智能股份有限公司 | Face recognition checking-in method and device thereof |
| US9251427B1 (en) | 2014-08-12 | 2016-02-02 | Microsoft Technology Licensing, Llc | False face representation identification |
| US9633269B2 (en) * | 2014-09-05 | 2017-04-25 | Qualcomm Incorporated | Image-based liveness detection for ultrasonic fingerprints |
| CN105472296B (en) * | 2014-09-09 | 2019-02-05 | 联想(北京)有限公司 | Real-time method of calibration and device |
| CN110276250B (en) * | 2015-01-12 | 2023-06-02 | 创新先进技术有限公司 | Face living body detection method and device |
| CN105868677B (en) * | 2015-01-19 | 2022-08-30 | 创新先进技术有限公司 | Living body face detection method and device |
| US10546189B2 (en) * | 2015-03-13 | 2020-01-28 | Nec Corporation | Living body detection device, living body detection method, and recording medium |
| CN105447441B (en) * | 2015-03-19 | 2019-03-29 | 北京眼神智能科技有限公司 | Face authentication method and device |
| CN104794386A (en) * | 2015-04-08 | 2015-07-22 | 天脉聚源(北京)传媒科技有限公司 | Data processing method and device based on face recognition |
| WO2016172872A1 (en) * | 2015-04-29 | 2016-11-03 | 北京旷视科技有限公司 | Method and device for verifying real human face, and computer program product |
| US10242441B2 (en) * | 2015-05-21 | 2019-03-26 | Koninklijke Philips N.V. | Identifying living skin tissue in a video sequence using color and spatial similarities |
| MX2017016718A (en) * | 2015-06-16 | 2018-11-09 | Eyeverify Inc | Systems and methods for spoof detection and liveness analysis. |
| WO2017000116A1 (en) * | 2015-06-29 | 2017-01-05 | 北京旷视科技有限公司 | Living body detection method, living body detection system, and computer program product |
| CN105119723A (en) * | 2015-09-15 | 2015-12-02 | 重庆智韬信息技术中心 | Identity authentication and authorization method based on human eye recognition |
| CN106557726B (en) * | 2015-09-25 | 2020-06-09 | 北京市商汤科技开发有限公司 | Face identity authentication system with silent type living body detection and method thereof |
| CN111144293A (en) * | 2015-09-25 | 2020-05-12 | 北京市商汤科技开发有限公司 | Human face identity authentication system with interactive living body detection and method thereof |
| CN106341380B (en) * | 2015-10-15 | 2018-02-16 | 收付宝科技有限公司 | A kind of methods, devices and systems for being used to carry out user long-distance identity-certifying |
| CN105426815A (en) * | 2015-10-29 | 2016-03-23 | 北京汉王智远科技有限公司 | Living body detection method and device |
| EP3369036A4 (en) * | 2015-10-30 | 2019-05-22 | Microsoft Technology Licensing, LLC | Spoofed face detection |
| CN105488486B (en) * | 2015-12-07 | 2018-10-30 | 清华大学 | The face identification method and device for preventing photo from attacking |
| CN105512632B (en) | 2015-12-09 | 2019-04-05 | 北京旷视科技有限公司 | Biopsy method and device |
| CN106897658B (en) * | 2015-12-18 | 2021-12-14 | 腾讯科技（深圳）有限公司 | Method and device for identifying human face living body |
| CN107408195B (en) * | 2015-12-29 | 2020-06-23 | 厦门中控智慧信息技术有限公司 | Iris identification method and device |
| CN105447483B (en) * | 2015-12-31 | 2019-03-22 | 徐州旷视数据科技有限公司 | Biopsy method and device |
| WO2017113286A1 (en) * | 2015-12-31 | 2017-07-06 | 深圳先进技术研究院 | Authentication method and apparatus |
| US9619723B1 (en) * | 2016-02-17 | 2017-04-11 | Hong Kong Applied Science and Technology Research Institute Company Limited | Method and system of identification and authentication using facial expression |
| US10111103B2 (en) | 2016-03-02 | 2018-10-23 | EyeVerify Inc. | Spoof detection using proximity sensors |
| CN105912908A (en) * | 2016-04-14 | 2016-08-31 | 苏州优化智能科技有限公司 | Infrared-based real person living body identity verification method |
| CN105893828A (en) * | 2016-05-05 | 2016-08-24 | 南京甄视智能科技有限公司 | Human face verification driving testing system and method based on mobile terminal |
| CN106096519A (en) * | 2016-06-01 | 2016-11-09 | 腾讯科技（深圳）有限公司 | Live body discrimination method and device |
| CN106127103B (en) * | 2016-06-12 | 2019-06-25 | 广州广电运通金融电子股份有限公司 | A kind of offline identity authentication method and device |
| CN106203320A (en) * | 2016-07-06 | 2016-12-07 | 惠州Tcl移动通信有限公司 | A kind of recognition of face optimization method based on mobile terminal and system |
| US10289822B2 (en) * | 2016-07-22 | 2019-05-14 | Nec Corporation | Liveness detection for antispoof face recognition |
| CN106407914B (en) * | 2016-08-31 | 2019-12-10 | 北京旷视科技有限公司 | Method and device for detecting human face and remote teller machine system |
| US10417473B2 (en) * | 2016-09-07 | 2019-09-17 | Mei-Yen Lee | Optical imaging system with variable light field for biometrics application |
| CN106372615A (en) * | 2016-09-19 | 2017-02-01 | 厦门中控生物识别信息技术有限公司 | Face anti-counterfeiting identification method and apparatus |
| CN106446855A (en) * | 2016-09-30 | 2017-02-22 | 深圳市商汤科技有限公司 | Real name authentication device |
| CN106599772B (en) * | 2016-10-31 | 2020-04-28 | 北京旷视科技有限公司 | Living body verification method and device and identity authentication method and device |
| CN106682578B (en) * | 2016-11-21 | 2020-05-05 | 北京交通大学 | Weak light face recognition method based on blink detection |
| CN106845916A (en) * | 2016-11-30 | 2017-06-13 | 浙江水马环保科技有限公司 | A kind of intelligent APP attendance management and method for monitoring state based on water purifier |
| CN106651302A (en) * | 2016-11-30 | 2017-05-10 | 浙江水马环保科技有限公司 | Intelligent PC attendance management and state monitoring method through water purifier |
| CN106845917A (en) * | 2016-11-30 | 2017-06-13 | 浙江水马环保科技有限公司 | A kind of intelligent APP Work attendance management systems based on water purifier |
| CN106845915A (en) * | 2016-11-30 | 2017-06-13 | 浙江水马环保科技有限公司 | A kind of water purifier intelligence PC Work attendance management systems |
| CN106778559A (en) * | 2016-12-01 | 2017-05-31 | 北京旷视科技有限公司 | The method and device of In vivo detection |
| CN106845345A (en) * | 2016-12-15 | 2017-06-13 | 重庆凯泽科技股份有限公司 | Biopsy method and device |
| CN108875473A (en) * | 2017-06-29 | 2018-11-23 | 北京旷视科技有限公司 | Living body verification method, device and system and storage medium |
| CN106682607A (en) * | 2016-12-23 | 2017-05-17 | 山东师范大学 | Offline face recognition system and offline face recognition method based on low-power-consumption embedded and infrared triggering |
| CN106650666A (en) * | 2016-12-26 | 2017-05-10 | 北京旷视科技有限公司 | Method and device for detection in vivo |
| CN108229326A (en) * | 2017-03-16 | 2018-06-29 | 北京市商汤科技开发有限公司 | Face false-proof detection method and system, electronic equipment, program and medium |
| CN112651348B (en) * | 2017-03-17 | 2024-04-05 | 北京旷视科技有限公司 | Identity authentication method and device and storage medium |
| CN106980836B (en) * | 2017-03-28 | 2020-07-03 | 北京小米移动软件有限公司 | Identity verification method and device |
| CN107273794A (en) * | 2017-04-28 | 2017-10-20 | 北京建筑大学 | Live body discrimination method and device in a kind of face recognition process |
| US10303932B2 (en) * | 2017-07-05 | 2019-05-28 | Midea Group Co., Ltd. | Face recognition in a residential environment |
| CN107506696A (en) | 2017-07-29 | 2017-12-22 | 广东欧珀移动通信有限公司 | Anti-fake processing method and related product |
| CN110909695B (en) * | 2017-07-29 | 2023-08-18 | Oppo广东移动通信有限公司 | Anti-counterfeiting processing method and related product |
| CN107454335B (en) * | 2017-08-31 | 2020-03-27 | Oppo广东移动通信有限公司 | Image processing method, image processing device, computer-readable storage medium and mobile terminal |
| CN107563329B (en) * | 2017-09-01 | 2021-03-30 | Oppo广东移动通信有限公司 | Image processing method, image processing device, computer-readable storage medium and mobile terminal |
| CN107679538B (en) * | 2017-09-05 | 2020-12-04 | 深圳大学 | Method and system for forming local feature descriptor of hyperspectral image |
| CN107590473B (en) * | 2017-09-19 | 2021-06-22 | 杭州登虹科技有限公司 | Human face living body detection method, medium and related device |
| CN107808115A (en) * | 2017-09-27 | 2018-03-16 | 联想(北京)有限公司 | A kind of biopsy method, device and storage medium |
| CN107729820B (en) * | 2017-09-27 | 2020-07-21 | 五邑大学 | Finger vein identification method based on multi-scale HOG |
| CN109684899A (en) * | 2017-10-18 | 2019-04-26 | 大猩猩科技股份有限公司 | A kind of face recognition method and system based on on-line study |
| CN107844763A (en) * | 2017-10-27 | 2018-03-27 | 努比亚技术有限公司 | A kind of face identification method, mobile terminal and computer-readable recording medium |
| CN107844764B (en) * | 2017-10-31 | 2020-05-12 | Oppo广东移动通信有限公司 | Image processing method, image processing device, electronic equipment and computer readable storage medium |
| CN107992811A (en) * | 2017-11-27 | 2018-05-04 | 北京小米移动软件有限公司 | Face identification method and device |
| CN107895155A (en) * | 2017-11-29 | 2018-04-10 | 五八有限公司 | A kind of face identification method and device |
| CN107909063B (en) * | 2017-12-22 | 2021-05-04 | 天津科技大学 | Biological identification video playback attack detection method based on gray level change |
| CN108171834A (en) * | 2017-12-25 | 2018-06-15 | 深圳禾思众成科技有限公司 | A kind of intelligent access control system |
| CN108197558A (en) * | 2017-12-28 | 2018-06-22 | 广东欧珀移动通信有限公司 | Face identification method, device, storage medium and electronic equipment |
| CN109993024A (en) * | 2017-12-29 | 2019-07-09 | 技嘉科技股份有限公司 | Authentication means, auth method and computer-readable storage medium |
| CN108268839A (en) * | 2018-01-05 | 2018-07-10 | 北京万相融通科技股份有限公司 | A kind of live body verification method and its system |
| CN108509857A (en) * | 2018-03-06 | 2018-09-07 | 达闼科技（北京）有限公司 | Human face in-vivo detection method, electronic equipment and computer program product |
| CN108564033A (en) * | 2018-04-12 | 2018-09-21 | Oppo广东移动通信有限公司 | Safe verification method, device based on structure light and terminal device |
| CN110929255A (en) * | 2018-04-12 | 2020-03-27 | Oppo广东移动通信有限公司 | Application program safety control method and device, mobile terminal and storage medium |
| US11410458B2 (en) | 2018-04-12 | 2022-08-09 | Guangdong Oppo Mobile Telecommunications Corp., Ltd. | Face identification method and apparatus, mobile terminal and storage medium |
| CN108596061A (en) * | 2018-04-12 | 2018-09-28 | Oppo广东移动通信有限公司 | Face identification method, device and mobile terminal, storage medium |
| CN108764091B (en) * | 2018-05-18 | 2020-11-17 | 北京市商汤科技开发有限公司 | Living body detection method and apparatus, electronic device, and storage medium |
| US10956714B2 (en) | 2018-05-18 | 2021-03-23 | Beijing Sensetime Technology Development Co., Ltd | Method and apparatus for detecting living body, electronic device, and storage medium |
| CN108470169A (en) * | 2018-05-23 | 2018-08-31 | 国政通科技股份有限公司 | Face identification system and method |
| CN108830058A (en) * | 2018-05-23 | 2018-11-16 | 平安科技（深圳）有限公司 | Safety certifying method, certificate server and computer readable storage medium |
| CN108647365A (en) * | 2018-05-29 | 2018-10-12 | 青岛九维华盾科技研究院有限公司 | A kind of camouflage of target effect evaluation method and system based on artificial interpretation |
| CN108764189A (en) * | 2018-06-04 | 2018-11-06 | 梁平 | A kind of vivo identification device |
| WO2019233199A1 (en) | 2018-06-06 | 2019-12-12 | Oppo广东移动通信有限公司 | Authentication method and device, and computer-readable storage medium |
| WO2020000368A1 (en) * | 2018-06-29 | 2020-01-02 | Logistics and Supply Chain MultiTech R&D Centre Limited | Electronic device and method for counting objects |
| CN109389794A (en) * | 2018-07-05 | 2019-02-26 | 北京中广通业信息科技股份有限公司 | A kind of Intellectualized Video Monitoring method and system |
| CN109117752A (en) * | 2018-07-24 | 2019-01-01 | 广州市国锐科技有限公司 | A kind of face recognition method based on gray scale and RGB |
| CN109147116A (en) * | 2018-07-25 | 2019-01-04 | 深圳市飞瑞斯科技有限公司 | The method that smart lock and control smart lock are opened |
| CN109086718A (en) * | 2018-08-02 | 2018-12-25 | 深圳市华付信息技术有限公司 | Biopsy method, device, computer equipment and storage medium |
| CN109165614A (en) * | 2018-08-31 | 2019-01-08 | 杭州行开科技有限公司 | Face identification system based on 3D camera |
| CN109151205A (en) * | 2018-09-04 | 2019-01-04 | 信利光电股份有限公司 | A kind of method, apparatus of connecting incoming call, communication apparatus and readable storage medium storing program for executing |
| US20200082160A1 (en) * | 2018-09-12 | 2020-03-12 | Kneron (Taiwan) Co., Ltd. | Face recognition module with artificial intelligence models |
| CN111046703B (en) | 2018-10-12 | 2023-04-18 | 杭州海康威视数字技术股份有限公司 | Face anti-counterfeiting detection method and device and multi-view camera |
| CN109543541B (en) * | 2018-10-23 | 2024-03-08 | 合肥的卢深视科技有限公司 | Living body detection method and device |
| CN111259698B (en) * | 2018-11-30 | 2023-10-13 | 百度在线网络技术（北京）有限公司 | Method and device for acquiring image |
| CN109598242B (en) * | 2018-12-06 | 2023-04-18 | 中科视拓（北京）科技有限公司 | Living body detection method |
| CN111382592B (en) * | 2018-12-27 | 2023-09-29 | 杭州海康威视数字技术股份有限公司 | Living body detection method and apparatus |
| JP7363455B2 (en) * | 2019-01-17 | 2023-10-18 | 株式会社デンソーウェーブ | Authentication system, authentication device and authentication method |
| CN111476072A (en) * | 2019-01-24 | 2020-07-31 | 大白科技发展(上海)有限公司 | Real-time passenger flow volume analysis system based on face recognition |
| CN109902597A (en) * | 2019-01-31 | 2019-06-18 | 杭州创匠信息科技有限公司 | Method for anti-counterfeit, device and system based on intravital blood vessel |
| CN109829434A (en) * | 2019-01-31 | 2019-05-31 | 杭州创匠信息科技有限公司 | Method for anti-counterfeit and device based on living body texture |
| CN109902604B (en) * | 2019-02-18 | 2023-05-02 | 麒麟软件有限公司 | High-safety face comparison system and method based on Feiteng platform |
| CN110110582A (en) * | 2019-03-14 | 2019-08-09 | 广州市金其利信息科技有限公司 | In conjunction with the face identification method and system of 3D structure light, infrared light and visible light |
| CN109934191A (en) * | 2019-03-20 | 2019-06-25 | 北京字节跳动网络技术有限公司 | Information processing method and device |
| CN110020520B (en) * | 2019-03-26 | 2021-03-26 | 华中科技大学 | Face recognition auxiliary authentication method and system based on sound signals |
| CN110110597B (en) * | 2019-04-02 | 2021-08-27 | 北京旷视科技有限公司 | Living body detection method and device and living body detection terminal |
| CN110059624B (en) * | 2019-04-18 | 2021-10-08 | 北京字节跳动网络技术有限公司 | Method and apparatus for detecting living body |
| CN110059638A (en) * | 2019-04-19 | 2019-07-26 | 中控智慧科技股份有限公司 | A kind of personal identification method and device |
| WO2020215229A1 (en) * | 2019-04-24 | 2020-10-29 | 深圳市汇顶科技股份有限公司 | Face registration method, face registration device, server and storage medium |
| WO2020223937A1 (en) * | 2019-05-08 | 2020-11-12 | 深圳市汇顶科技股份有限公司 | Face recognition method, face recognition device and computer readable storage medium |
| WO2020237482A1 (en) * | 2019-05-27 | 2020-12-03 | 深圳市汇顶科技股份有限公司 | Optical sensor, apparatus and method for facial recognition, and electronic device |
| WO2020237483A1 (en) * | 2019-05-27 | 2020-12-03 | 深圳市汇顶科技股份有限公司 | Optical sensor, apparatus and method for facial recognition, and electronic device |
| CN110276313B (en) * | 2019-06-25 | 2022-04-22 | 杭州网易智企科技有限公司 | Identity authentication method, identity authentication device, medium and computing equipment |
| CN110411570B (en) * | 2019-06-28 | 2020-08-28 | 武汉高德智感科技有限公司 | Infrared human body temperature screening method based on human body detection and human body tracking technology |
| CN110298335A (en) * | 2019-07-09 | 2019-10-01 | 中控华运（厦门）集成电路有限公司 | Face identification system and face identification method |
| US11200333B2 (en) | 2019-08-13 | 2021-12-14 | International Business Machines Corporation | File exposure to an intended recipient |
| CN110532907B (en) * | 2019-08-14 | 2022-01-21 | 中国科学院自动化研究所 | Traditional Chinese medicine human body constitution classification method based on face image and tongue image bimodal feature extraction |
| CN112395929A (en) * | 2019-08-19 | 2021-02-23 | 扬州盛世云信息科技有限公司 | Face living body detection method based on infrared image LBP histogram characteristics |
| WO2021046773A1 (en) * | 2019-09-11 | 2021-03-18 | 深圳市汇顶科技股份有限公司 | Facial anti-counterfeiting detection method and apparatus, chip, electronic device and computer-readable medium |
| CN110929557A (en) * | 2019-09-25 | 2020-03-27 | 四川大学锦城学院 | Intelligent security method, system and processing device based on in-vivo detection |
| CN112733575A (en) * | 2019-10-14 | 2021-04-30 | 北京字节跳动网络技术有限公司 | Image processing method, image processing device, electronic equipment and storage medium |
| CN111008651B (en) * | 2019-11-13 | 2023-04-28 | 科大国创软件股份有限公司 | Image reproduction detection method based on multi-feature fusion |
| CN111191519B (en) * | 2019-12-09 | 2023-11-24 | 同济大学 | Living body detection method for user access of mobile power supply device |
| CN113051982B (en) * | 2019-12-28 | 2024-04-02 | 浙江宇视科技有限公司 | Face living body detection method, device, equipment and storage medium |
| CN111222433B (en) * | 2019-12-30 | 2023-06-20 | 新大陆数字技术股份有限公司 | Automatic face auditing method, system, equipment and readable storage medium |
| CN111259757B (en) * | 2020-01-13 | 2023-06-20 | 支付宝实验室(新加坡)有限公司 | Living body identification method, device and equipment based on image |
| CN113128320B (en) * | 2020-01-16 | 2023-05-16 | 浙江舜宇智能光学技术有限公司 | Human face living body detection method and device based on TOF camera and electronic equipment |
| CN111259814B (en) * | 2020-01-17 | 2023-10-31 | 杭州涂鸦信息技术有限公司 | Living body detection method and system |
| CN111242090B (en) * | 2020-01-22 | 2023-06-23 | 腾讯科技（深圳）有限公司 | Human face recognition method, device, equipment and medium based on artificial intelligence |
| CN111368665B (en) * | 2020-02-25 | 2022-08-02 | 西华大学 | High-anti-spoofing multi-mode identity authentication method based on competition and cooperation mechanism |
| CN111339958B (en) * | 2020-02-28 | 2023-08-29 | 南京鑫之派智能科技有限公司 | Face living body detection method and system based on monocular vision |
| CN111414831B (en) * | 2020-03-13 | 2022-08-12 | 深圳市商汤科技有限公司 | Monitoring method and system, electronic device and storage medium |
| CN112269975A (en) * | 2020-03-31 | 2021-01-26 | 周亚琴 | Internet of things artificial intelligence face verification method and system and Internet of things cloud server |
| TWI722872B (en) | 2020-04-17 | 2021-03-21 | 技嘉科技股份有限公司 | Face recognition device and face recognition method |
| CN111723651B (en) * | 2020-05-09 | 2023-10-10 | 熵基科技股份有限公司 | Face recognition method, face recognition device and terminal equipment |
| CN111858625A (en) * | 2020-05-14 | 2020-10-30 | 深圳力维智联技术有限公司 | Resident housing state monitoring method, resident housing state monitoring device, resident housing state monitoring equipment and readable storage medium |
| CN111368811B (en) * | 2020-05-26 | 2020-09-18 | 腾讯科技（深圳）有限公司 | Living body detection method, living body detection device, living body detection equipment and storage medium |
| CN111695509A (en) * | 2020-06-12 | 2020-09-22 | 云从科技集团股份有限公司 | Identity authentication method, identity authentication device, machine readable medium and equipment |
| CN111739046A (en) * | 2020-06-19 | 2020-10-02 | 百度在线网络技术（北京）有限公司 | Method, apparatus, device and medium for model update and image detection |
| CN111967296B (en) * | 2020-06-28 | 2023-12-05 | 北京中科虹霸科技有限公司 | Iris living body detection method, access control method and device |
| CN111783647B (en) * | 2020-06-30 | 2023-11-03 | 北京百度网讯科技有限公司 | Training method of face fusion model, face fusion method, device and equipment |
| TWI790459B (en) * | 2020-07-09 | 2023-01-21 | 立普思股份有限公司 | Infrared recognition method for human body |
| CN112001240B (en) * | 2020-07-15 | 2021-08-17 | 浙江大华技术股份有限公司 | Living body detection method, living body detection device, computer equipment and storage medium |
| CN111968152B (en) * | 2020-07-15 | 2023-10-17 | 桂林远望智能通信科技有限公司 | Dynamic identity recognition method and device |
| CN112036238A (en) * | 2020-07-24 | 2020-12-04 | 支付宝实验室(新加坡)有限公司 | Face data processing method and device, electronic equipment and storage medium |
| CN112001262B (en) * | 2020-07-28 | 2022-07-29 | 山东师范大学 | Method for generating accessory capable of influencing face authentication |
| CN112052759B (en) * | 2020-08-25 | 2022-09-09 | 腾讯科技（深圳）有限公司 | Living body detection method and device |
| CN112215136B (en) * | 2020-10-10 | 2023-09-05 | 北京奇艺世纪科技有限公司 | Target person identification method and device, electronic equipment and storage medium |
| WO2022084824A1 (en) * | 2020-10-21 | 2022-04-28 | Gopi Yadavaprasath | System and method for face anti-counterfeiting |
| CN112487921B (en) * | 2020-11-25 | 2023-09-08 | 奥比中光科技集团股份有限公司 | Face image preprocessing method and system for living body detection |
| US11443527B2 (en) | 2021-01-13 | 2022-09-13 | Ford Global Technologies, Llc | Material spectroscopy |
| US11741747B2 (en) | 2021-01-13 | 2023-08-29 | Ford Global Technologies, Llc | Material spectroscopy |
| US11657589B2 (en) | 2021-01-13 | 2023-05-23 | Ford Global Technologies, Llc | Material spectroscopy |
| CN113158818B (en) * | 2021-03-29 | 2023-04-07 | 青岛海尔科技有限公司 | Method, device and equipment for identifying fake video |
| CN113297976B (en) * | 2021-05-26 | 2023-06-23 | 中国铁塔股份有限公司大连市分公司 | Base station intrusion detection method and system based on deep learning |
| CN113657293B (en) * | 2021-08-19 | 2023-11-24 | 北京神州新桥科技有限公司 | Living body detection method, living body detection device, electronic equipment, medium and program product |
| CN114896575B (en) * | 2022-04-28 | 2024-04-16 | 西安电子科技大学 | Trusted identity identification method and device based on false attribute detection |
| CN114894337B (en) * | 2022-07-11 | 2022-09-27 | 深圳市大树人工智能科技有限公司 | Temperature measurement method and device for outdoor face recognition |
| CN117877098B (en) * | 2024-03-07 | 2024-05-28 | 深圳百沃彰世科技有限公司 | Abnormal communication behavior identification method and system based on big data and multidimensional features |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101702198B (en) * | 2009-11-19 | 2011-11-23 | 浙江大学 | Identification method for video and living body faces based on background comparison |
| CN101770613A (en) * | 2010-01-19 | 2010-07-07 | 北京智慧眼科技发展有限公司 | Social insurance identity authentication method based on face recognition and living body detection |
| US8675926B2 (en) * | 2010-06-08 | 2014-03-18 | Microsoft Corporation | Distinguishing live faces from flat surfaces |
| CN101964056B (en) * | 2010-10-26 | 2012-06-27 | 徐勇 | Bimodal face authentication method with living body detection function and system |
| CN102622588B (en) * | 2012-03-08 | 2013-10-09 | 无锡中科奥森科技有限公司 | Dual-certification face anti-counterfeit method and device |
-
2012
- 2012-03-08 CN CN2012100594547A patent/CN102622588B/en active Active
-
2013
- 2013-03-05 WO PCT/CN2013/000228 patent/WO2013131407A1/en active Application Filing
Also Published As
| Publication number | Publication date |
|---|---|
| CN102622588A (en) | 2012-08-01 |
| WO2013131407A1 (en) | 2013-09-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102622588B (en) | Dual-certification face anti-counterfeit method and device | |
| CN108921100B (en) | Face recognition method and system based on visible light image and infrared image fusion | |
| CN105518711B (en) | Biopsy method, In vivo detection system and computer program product | |
| CN106874871B (en) | Living body face double-camera identification method and identification device | |
| CN105574518B (en) | Method and device for detecting living human face | |
| CN106407914B (en) | Method and device for detecting human face and remote teller machine system | |
| CN100361135C (en) | Method for acquiring human-face image, human-face discrimination and discriminating system | |
| US7881524B2 (en) | Information processing apparatus and information processing method | |
| CN103440479B (en) | A kind of method and system for detecting living body human face | |
| CN109101871A (en) | A kind of living body detection device based on depth and Near Infrared Information, detection method and its application | |
| CN101493884B (en) | Multi-optical spectrum image collecting device and method | |
| CN107862299A (en) | A kind of living body faces detection method based on near-infrared Yu visible ray binocular camera | |
| CN102262727A (en) | Method for monitoring face image quality at client acquisition terminal in real time | |
| CN107122744A (en) | A kind of In vivo detection system and method based on recognition of face | |
| CN107392187B (en) | Face in-vivo detection method based on gradient direction histogram | |
| CN103383723A (en) | Method and system for spoof detection for biometric authentication | |
| CN111462379A (en) | Access control management method, system and medium containing palm vein and face recognition | |
| CN105975938A (en) | Smart community manager service system with dynamic face identification function | |
| CN110309782A (en) | It is a kind of based on infrared with visible light biocular systems living body faces detection methods | |
| CN106372615A (en) | Face anti-counterfeiting identification method and apparatus | |
| CN103996033B (en) | Human identification method based on tracking of human face five-sense-organ coordinates | |
| CN111582197A (en) | Living body based on near infrared and 3D camera shooting technology and face recognition system | |
| CN109255319A (en) | For the recognition of face payment information method for anti-counterfeit of still photo | |
| CN104615993B (en) | The certification device and method that a kind of finger vena identification combines with face hand vein recognition | |
| CN107909063A (en) | Bio-identification video playback attack detection method based on grey scale change |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C53 | Correction of patent of invention or patent application | ||
| CB02 | Change of applicant information |
Address after: 214135 Jiangsu New District of Wuxi City Linghu Road No. 200 China Sensor Network International Innovation Park building C Applicant after: AuthenMetric Co.Ltd Address before: 214135 Jiangsu New District of Wuxi City Linghu Road No. 200 China Sensor Network International Innovation Park building C Applicant before: AuthenMetric Co.Ltd |
|
| COR | Change of bibliographic data |
Free format text: CORRECT: APPLICANT; FROM: WUXI DIGIT AUTHENMETRIC TECHNOLOGY CO. LTD. TO: AUTHENMETRIC CO. LTD. |
|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C41 | Transfer of patent application or patent right or utility model | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20160311 Address after: 100190 Zhongguancun East Road, Beijing, No. 95, No. Patentee after: Institute of Automation, Chinese Academy of Sciences Address before: 214135 Jiangsu New District of Wuxi City Linghu Road No. 200 China Sensor Network International Innovation Park building C Patentee before: AuthenMetric Co.Ltd |
|
| C41 | Transfer of patent application or patent right or utility model | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20160830 Address after: 100191, Haidian District, Zhichun Road, Beijing No. 7 to the real building, block D, 17, 1702 Patentee after: Beijing Keaosen Data Technology Co. Ltd. Address before: 100190 Zhongguancun East Road, Beijing, No. 95, No. Patentee before: Institute of Automation, Chinese Academy of Sciences |