CN108229362A - A kind of binocular recognition of face biopsy method based on access control system - Google Patents
A kind of binocular recognition of face biopsy method based on access control system Download PDFInfo
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Abstract
The invention discloses a kind of binocular recognition of face biopsy methods based on access control system.It specifically comprises the following steps：(1) video image acquisition is carried out using binocular acquisition system；(2) Face datection is carried out to collected color video frequency image；(3) if there is detecting face, then color video frequency image and Infrared video image are analyzed；(4) classification and Detection is carried out to photo and live body using the SVM In vivo detections grader of machine learning；(5) collected face and face bottom library are subjected to match cognization, if successful match, illustrate recognition of face success, access control system control is opened the door；If matching is unsuccessful, access control system is not opened the door.The beneficial effects of the invention are as follows：Not only cost is small, but also algorithm speed is fast, and algorithm effect can also be guaranteed；In addition, the analysis of cooperation coloured image and infrared image can largely improve the Detection accuracy of In vivo detection, do not need to user's cooperation and act, more rapid and convenient.
Description
Technical field
The present invention relates to Biometrics correlative technology fields, refer in particular to a kind of binocular face based on access control system
Identify biopsy method.
Background technology
With the development of biological identification technology and mode identification technology, face recognition technology has tended to be ripe, recognition of face
It system and can be very good to carry out human face detection and tracing, but for access control system, user can take advantage of by photo
System is deceived, therefore, for drawbacks described above present in currently available technology, it is really necessary to be studied, to provide a kind of scheme,
Defect in the prior art is solved, avoids the crisis for causing to break through access control system using photo.
The three classes scheme that existing binocular recognition of face In vivo detection mainly uses：1. interoperation carries out In vivo detection,
User needs to coordinate a series of required movements such as shaken the head, blinked, opened one's mouth, and can just determine whether live body.2. utilize image
Algorithm judges whether the video of monocular cam acquisition is live body, since the living body faces and photo of camera acquisition are all two dimensions
Image, simple image algorithm are difficult to distinguish that determine bottom be photo or face, and verification and measurement ratio is low.3. it is carried out using depth camera
3D modeling is to determine whether for live body, but this method not only needs to increase depth camera, and depth camera costliness cost is high, and
3D modeling algorithm is complicated, and arithmetic speed is slow.
Invention content
The present invention is above-mentioned in order to overcome the shortcomings of to exist in the prior art, and provides a kind of detection efficiency height and detection is accurate
The high binocular recognition of face biopsy method based on access control system of true rate.
To achieve these goals, the present invention uses following technical scheme：
A kind of binocular recognition of face biopsy method based on access control system, specifically comprises the following steps：
(1) video image acquisition is carried out using binocular acquisition system, acquires color video frequency image and infrared video respectively
Image；
(2) Face datection is carried out to collected color video frequency image, next step analysis is carried out if face is detected,
If not detecting face, illustrate for non-living body, step termination, without recognition of face；
(3) color video frequency image and Infrared video image are analyzed, if meeting color video frequency image and red simultaneously
The condition of outer video image then carries out in next step, being otherwise judged as black-and-white photograph non-living body, and step terminates, and knows without face
Not；
(4) classification and Detection is carried out to photo and live body using the SVM In vivo detections grader of machine learning, if it is determined that
Live body then carries out the face alignment of next step, if photo, then explanation is non-living body, and step terminates；
(5) collected face and face bottom library are subjected to match cognization, if successful match, illustrate recognition of face into
Work(, is validated user, and access control system control is opened the door；If matching is unsuccessful, illustrate that recognition of face fails, be illegal use
Family, access control system are not opened the door.
The present invention mainly analyzes color video frequency image and Infrared video image using image algorithm, due to acquisition
The video image that system acquires black-and-white photograph and living body faces has obvious difference on characteristics of image, therefore from image
Whether it is black-and-white photograph that feature distinguishes, and excludes attack of the black-and-white photograph to system.Although image analysis module can exclude black
White attack of the photo to system, but attack of the photochrome to system is cannot exclude, so the svm classifier using machine learning
Device carries out classification and Detection to photo and live body.Present invention employs binocular cameras, and not only cost is small, but also algorithm speed is fast,
Algorithm effect can also be guaranteed；In addition, the analysis of cooperation coloured image and infrared image can largely improve live body
The Detection accuracy of detection does not need to user's cooperation and acts, more rapid and convenient.
Preferably, in step (1), binocular acquisition system includes having the camera of colour imagery shot and has infrared take the photograph
As the camera of head, two cameras carry out video image acquisition, wherein：Have the camera acquisition color video figure of colour imagery shot
Picture, has the camera acquisition Infrared video image of infrared camera, and two cameras are located at same parallel lines and acquire simultaneously.Its
In：Parallel camera is more advantageous to the production of hardware device compared to other positions such as arc, and hardware is advantageously integrated binocular camera, reduces
Production cost, and the video shot of parallel camera is conducive to the modeling of algorithm, algorithm model complexity is low, promotes operation speed
Degree.
Preferably, in step (2), Face datection is calculated using classical machine learning algorithm Adaboost Face datections
Method.
Preferably, in step (3), the condition for meeting color video frequency image is：The RGB component of color video frequency image
Similarity is more than threshold value T1；The condition for meeting Infrared video image is：The histogram contrast C of Infrared video image is more than threshold value
The calculation formula of T2, C are as follows：
C=∑s [δ (i, j)]2P (i, j)；Wherein：T1 is empirical value, and T2 is empirical value, δ (i, j)=| i-j |, as phase
Gray scale difference between adjacent pixel, the pixel distribution probability of gray scale differences of the P (i, j) between adjacent pixel.Wherein color video frequency image
The similarity of RGB component is more than the principle of threshold value T1：If black-and-white photograph, then color camera acquisition image each color
Component is relatively, much like, the general value 0.78 of threshold value of T1, can be according to being adjusted with usage scenario.Infrared figure
It is red if target (photo) with the temperature difference of background opposite live body of the non-living body then in scene is low as generally referring to thermal imaging
The dynamic range of outer image is big, and contrast is low, judges live body and photo, the general values of T2 according to the algorithm of this characteristic Design
1.8。
Preferably, in step (4), learnt using two groups of samples of photochrome and living body faces come training machine
SVM In vivo detection graders.
Preferably, in step (5), it is as follows：Face alignment is carried out using color video frequency image, will be acquired
To face and face bottom library in all faces carry out one score value of similarity calculation, if highest score be more than 80 points,
Illustrate face alignment success, matched face is the bottom library face of highest scoring, and recognition of face is successful, for validated user, gate inhibition
System control is opened the door；Otherwise, face alignment fails, and is illegal user, and access control system is not opened the door.Wherein：Value 80 is divided the most
Properly, because if setting is excessive, discrimination can be caused to reduce, some validated users are because the acquisitions such as illumination or side face are shone
Illegal user is mistaken in the case of tablet quality is relatively low；The people that can cause some appearance similar if too low is misidentified into other
People.
The beneficial effects of the invention are as follows：Binocular camera is employed, not only cost is small, but also algorithm speed is fast, algorithm effect
Fruit can also be guaranteed；In addition, the analysis of cooperation coloured image and infrared image can largely improve In vivo detection
Detection accuracy does not need to user's cooperation and acts, more rapid and convenient.
Description of the drawings
Fig. 1 is flow chart of the method for the present invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and detailed description.
In embodiment as described in Figure 1, a kind of binocular recognition of face biopsy method based on access control system is specific to wrap
Include following steps：
(1) video image acquisition is carried out using binocular acquisition system, acquires color video frequency image and infrared video respectively
Image；
Binocular acquisition system includes having the camera of colour imagery shot and has the camera of infrared camera, and two cameras come
Video image acquisition is carried out, wherein：The camera acquisition color video frequency image of colour imagery shot is had, has the phase of infrared camera
Machine acquires Infrared video image, and two cameras are located at same parallel lines and acquire simultaneously；Parallel camera compares other positions such as arc
Shape is more advantageous to the production of hardware device, and hardware is advantageously integrated what binocular camera, reduction production cost, and parallel camera were shot
Video is conducive to the modeling of algorithm, and algorithm model complexity is low, improving operational speed；
(2) Face datection is carried out to collected color video frequency image, Face datection is using classical machine learning algorithm
Adaboost Face datection algorithms, carry out next step analysis if face is detected, if not detecting face, illustrate
For non-living body, step terminates, without recognition of face；
(3) color video frequency image and Infrared video image are analyzed, if meeting color video frequency image and red simultaneously
The condition of outer video image then carries out in next step, being otherwise judged as black-and-white photograph non-living body, and step terminates, and knows without face
Not；
(4) classification and Detection is carried out to photo and live body using the SVM In vivo detections grader of machine learning, if it is determined that
Live body then carries out the face alignment of next step, if photo, then explanation is non-living body, and step terminates；
The condition for meeting color video frequency image is：The similarity of the RGB component of color video frequency image is more than threshold value T1；If
Black-and-white photograph, then color camera acquisition image each color component relatively, much like, the general value of threshold value of T1
0.78, it can be according to being adjusted with usage scenario；
The condition for meeting Infrared video image is：The histogram contrast C of Infrared video image is more than the meter of threshold value T2, C
It is as follows to calculate formula：C=∑s [δ (i, j)]2P (i, j)；Infrared image generally refers to thermal imaging, if non-living body is then in scene
Target (photo) live body opposite with the temperature difference of background is low, and the dynamic range of infrared image is big, and contrast is low, according to this feature
The algorithm of design judges live body and photo, the general value 1.8 of threshold value of T2；
Wherein：T1 is empirical value, and T2 is empirical value, δ (i, j)=| i-j |, the as gray scale difference between adjacent pixel, P (i,
J) the pixel distribution probability of the gray scale difference between adjacent pixel；
(5) collected face and face bottom library are subjected to match cognization, if successful match, illustrate recognition of face into
Work(, is validated user, and access control system control is opened the door；If matching is unsuccessful, illustrate that recognition of face fails, be illegal use
Family, access control system are not opened the door；
It is as follows：Face alignment is carried out using color video frequency image, it will be in collected face and face bottom library
All faces carry out one score value of similarity calculation, if highest score be more than 80 points, illustrate face alignment success, matching
Face be highest scoring bottom library face, recognition of face success, be validated user, access control system control open the door；Otherwise, face
Failure is compared, is illegal user, access control system is not opened the door.Wherein：It is the most suitable that value 80 is divided, because if setting is excessive then
Discrimination can be caused to reduce, some validated users because illumination or side face etc. acquisition photographic quality it is relatively low in the case of be mistaken for it is non-
Validated user；The people that can cause some appearance similar if too low is misidentified into other people.
The present invention mainly analyzes color video frequency image and Infrared video image using image algorithm, due to acquisition
The video image that system acquires black-and-white photograph and living body faces has obvious difference on characteristics of image, therefore from image
Whether it is black-and-white photograph that feature distinguishes, and excludes attack of the black-and-white photograph to system.Although image analysis module can exclude black
White attack of the photo to system, but attack of the photochrome to system is cannot exclude, so the svm classifier using machine learning
Device carries out classification and Detection to photo and live body.Present invention employs binocular cameras, and not only cost is small, but also algorithm speed is fast,
Algorithm effect can also be guaranteed；In addition, the analysis of cooperation coloured image and infrared image can largely improve live body
The Detection accuracy of detection does not need to user's cooperation and acts, more rapid and convenient.
Claims (6)
1. a kind of binocular recognition of face biopsy method based on access control system, it is characterized in that, specifically comprise the following steps：
(1) video image acquisition is carried out using binocular acquisition system, acquires color video frequency image and Infrared video image respectively；
(2) Face datection is carried out to collected color video frequency image, next step analysis is carried out if face is detected, if
Do not detect face, then explanation is non-living body, and step terminates, without recognition of face；
(3) color video frequency image and Infrared video image are analyzed, if meeting color video frequency image simultaneously and infrared regarding
The condition of frequency image then carries out in next step, being otherwise judged as black-and-white photograph non-living body, step terminates, without recognition of face；
(4) classification and Detection is carried out to photo and live body using the SVM In vivo detections grader of machine learning, if it is determined that living
Body then carries out the face alignment of next step, if photo, then explanation is non-living body, and step terminates；
(5) collected face and face bottom library are subjected to match cognization, if successful match, illustrate recognition of face success,
For validated user, access control system control is opened the door；If matching is unsuccessful, illustrate that recognition of face fails, for illegal user, door
Access control system does not open the door.
2. a kind of binocular recognition of face biopsy method based on access control system according to claim 1, it is characterized in that,
In step (1), binocular acquisition system includes having the camera of colour imagery shot and has the camera of infrared camera, two phases
Machine carries out video image acquisition, wherein：The camera acquisition color video frequency image of colour imagery shot is had, has infrared camera
Camera acquisition Infrared video image, two cameras are located at same parallel lines and acquire simultaneously.
3. a kind of binocular recognition of face biopsy method based on access control system according to claim 1, it is characterized in that,
In step (2), Face datection is using classical machine learning algorithm Adaboost Face datection algorithms.
4. a kind of binocular recognition of face biopsy method based on access control system according to claim 1, it is characterized in that,
In step (3), the condition for meeting color video frequency image is：The similarity of the RGB component of color video frequency image is more than threshold value T1；
The condition for meeting Infrared video image is：Calculation formula of the histogram contrast C of Infrared video image more than threshold value T2, C is such as
Under：C=∑s [δ (i, j)]2P (i, j)；Wherein：T1 is empirical value, and T2 is empirical value, δ (i, j)=| i-j |, as adjacent pixel
Between gray scale difference, the pixel distribution probability of gray scale differences of the P (i, j) between adjacent pixel.
5. a kind of binocular recognition of face biopsy method based on access control system according to claim 1, it is characterized in that,
In step (4), classified using two groups of samples of photochrome and living body faces come the SVM In vivo detections that training machine learns
Device.
6. a kind of binocular recognition of face biopsy method based on access control system according to claim 1, it is characterized in that,
In step (5), it is as follows：Face alignment is carried out using color video frequency image, by collected face and face bottom
All faces in library carry out one score value of similarity calculation, if highest score is more than 80 points, illustrate face alignment success,
Matched face is the bottom library face of highest scoring, and recognition of face success is validated user, and access control system control is opened the door；Otherwise,
Face alignment fails, and is illegal user, and access control system is not opened the door.
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