CN103593598B - User's on-line authentication method and system based on In vivo detection and recognition of face - Google Patents
User's on-line authentication method and system based on In vivo detection and recognition of face Download PDFInfo
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- CN103593598B CN103593598B CN201310602042.8A CN201310602042A CN103593598B CN 103593598 B CN103593598 B CN 103593598B CN 201310602042 A CN201310602042 A CN 201310602042A CN 103593598 B CN103593598 B CN 103593598B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F2221/2133—Verifying human interaction, e.g., Captcha
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Abstract
Present invention is disclosed a kind of user's on-line authentication method and system based on In vivo detection and recognition of face, described method includes user's online registration step, user's on-line authentication step；User's on-line authentication step includes In vivo detection step, image processing step, characteristics extraction step, face alignment step, result treatment step；In vivo detection step confirming, whether certification user is live body and obtains human face photo；The human face photo gathered is processed by image processing step；To the human face photo processed in characteristic extraction step, extract face component feature；In face alignment step, the characteristic of the facial image of the collection of extraction and the corresponding human face data in described user's face characteristic Value Data storehouse, by setting a threshold value, when similarity exceedes this threshold value, then result coupling obtained exports.The present invention can avoid using the video containing face to gain certification by cheating, improves security of system；Recognition time can be shortened simultaneously, improve recognition accuracy.
Description
Technical field
The invention belongs to computer and technical field of face recognition, relate to a kind of on-line authentication identification side
Method, particularly relates to a kind of user's on-line authentication method based on In vivo detection and recognition of face；Meanwhile,
The invention still further relates to a kind of user's on-line authentication system based on In vivo detection and recognition of face.
Background technology
In current information-intensive society, the information technology with cyber-net as representative almost penetrates into life
Every aspect.But we are while enjoying " information ", have also brought dangerous
Shade, so people are in being engaged in vairious activities, it is often necessary to carry out the certification of personal identification,
Thus the safety of guarantee information.Because the naturality of face, stability, easily collection property, and quilt
It is applied to authentication.
Existing verification process refers to Fig. 1, mainly includes two stages: when user's online registration,
Need to gather user's human face photo, extract face characteristic and put it in characteristic value data storehouse；?
During user's on-line authentication, gather user's human face photo, then carry out Face datection, image procossing,
Face characteristic extracts, and by face corresponding with characteristic value data storehouse for the face characteristic that extracts
Feature is compared, and then obtains a result.
In place of existing authentication method Shortcomings, specifically include that one, at camera collection photo mould
Block, it is impossible to screen image/video or true man；Its two, this method is at recognition speed and discrimination
Do not meet the real demand of user.
In view of this, nowadays in the urgent need to designing a kind of new on-line authentication mode, in order to overcome existing
The drawbacks described above of authentication method.
Summary of the invention
The technical problem to be solved is: provide a kind of based on In vivo detection with recognition of face
User's on-line authentication method, can avoid using the video containing face to gain certification by cheating, improve system
Safety；Recognition time can be shortened simultaneously, improve recognition accuracy.
Additionally, the present invention also provides for a kind of user of based on In vivo detection and recognition of face on-line authentication system
System, can avoid using the video containing face to gain certification by cheating, improve security of system；Simultaneously can
To shorten recognition time, improve recognition accuracy.
For solving above-mentioned technical problem, the present invention adopts the following technical scheme that
A kind of user's on-line authentication method based on In vivo detection and recognition of face, described method includes:
Step S10, user's online registration step: the online fill message of user, and according to the body submitted to
Part card number transfers the Certification of Second Generation photo of correspondence by public security Intranet, extracts photo face characteristic value,
Set up user's face characteristic Value Data storehouse；
Step S20, user's on-line steps；Including In vivo detection step, image processing step, feature
Extraction step, face alignment step, result treatment step；Specifically include:
-step S21, In vivo detection step, be confirmed whether it is live body and the optimum human face photo of acquisition；
Based on head rotation direction as the determining program of instruction, only turned by head within the setting time
Dynamic drive bead reaches to specify position, then be judged as live body, can authenticate；Choose the anglec of rotation simultaneously
The photo of degree minimum is optimum human face photo；In vivo detection step includes Face datection and human face posture
Detection；
In Face datection step, it is determined whether be face and structures locating；To camera collection to each
Two field picture, carries out greyscale transformation, Filtering Processing, it is thus achieved that high-quality gray-scale map；Gray-scale map is utilized
Integration quickly calculates Harr-Like wavelet character value, is applied to off-line training good
AdaBoost-Cascade grader, it determines whether be face；Face shape facility according to face
With AdaBoost-Cascade grader the region of face window carried out eyes, double eyebrow, nose,
Face and lower jaw location, determine human face position；
In human face posture detecting step, use based on oval template and the Attitude estimation side of face position
Method；First pass through Face datection and determine the position of eyes, nose and face, then to detecting
Face connected domain border carries out ellipse fitting and obtains oval template, calculates eyes, face and nose
Location parameter in a template, finally sends into the appearance that three-layer artificial neural network obtains by location parameter
The rough estimate value of state parameter；For improving the precision of Attitude estimation, according to rough estimate result by defeated
Enter image and send into corresponding linear correlation wave filter after certain process, obtain relatively accurate people
Face Attitude estimation result；
-step S22, image processing step；The human face photo collecting optimum attitude carries out light benefit
Repay, greyscale transformation, histogram equalization, normalization, geometric correction, filter and sharpen, service
In feature extraction；
-step S23, characteristic extraction step；To the human face photo processed, extract face component special
Levy, including naked face, eyebrow, eyes, mouth face component, utilize principal component method to extract people
Face component feature；
-step S24, face alignment step；The characteristic and two of the facial image of the collection extracted
Generation card human face data, by setting a threshold value, when similarity exceedes this threshold value, then coupling
The result output obtained；
-step S25, result treatment step；As result is mated, then prompting " certification success ", with
Time extract face characteristic value and be saved in server user's face database；As result is not mated, then
Prompting " re-authentication ", restarts photo acquisition.
A kind of user's on-line authentication method based on In vivo detection and recognition of face, described method includes:
Step S10, user's online registration step: the online fill message of user, obtain corresponding face special
Value indicative, sets up user's face characteristic Value Data storehouse；
Step S20, user's on-line authentication step；Including In vivo detection step, image processing step,
Characteristic extraction step, face alignment step, result treatment step；Specifically include:
-step S21, In vivo detection step, confirm whether certification user is live body and obtains face photograph
Sheet；
-step S22, image processing step；The human face photo gathered is processed；
-step S23, characteristic extraction step；To the human face photo processed, extract face component special
Levy；
-step S24, face alignment step；The characteristic of the facial image of the collection extracted and institute
State the corresponding human face data in user's face characteristic Value Data storehouse, by setting a threshold value, work as phase
Exceed this threshold value like degree, then result coupling obtained exports；
-step S25, result treatment step；Respective handling is made according to face alignment result.
As a preferred embodiment of the present invention, in described step S21, it may be judged whether for the side of live body
Method is: based on head rotation direction as the determining program of instruction, only driven by head rotation
Object reaches to specify position, then be judged as that live body chooses the minimum photo of the anglec of rotation for optimum simultaneously
Human face photo.
As a preferred embodiment of the present invention, the In vivo detection step in described step S21 includes people
Face detection and human face posture detection；
In Face datection step, it is determined whether be face and structures locating；To camera collection to each
Two field picture, carries out greyscale transformation, Filtering Processing, it is thus achieved that high-quality gray-scale map；
Gray-scale map utilizes integration quickly calculate Harr-Like wavelet character value, is applied to off-line instruction
The AdaBoost-Cascade grader perfected, it determines whether be face；
Face shape facility according to face and AdaBoost-Cascade grader are to face window
Region carries out eyes, double eyebrow, nose, face and lower jaw location, determines human face position；
In human face posture detecting step, use based on oval template and the Attitude estimation side of face position
Method；
First pass through Face datection and determine the position of eyes, nose and face, then to the people detected
Face connected domain border carries out ellipse fitting and obtains oval template, calculate eyes, face and nose
Location parameter in template, finally sends into the attitude that three-layer artificial neural network obtains by location parameter
The rough estimate value of parameter；
For improving the precision of Attitude estimation, according to rough estimate result, input picture is sent after treatment
Enter corresponding linear correlation wave filter, obtain relatively accurate human face modeling result.
As a preferred embodiment of the present invention, in step S22, the face collecting optimum attitude shines
Sheet carries out light compensation, greyscale transformation, histogram equalization, normalization, geometric correction, filtering
And sharpen, serve feature extraction；
In step S23, the face component feature of extraction includes naked face, eyebrow, eyes, mouth face
Part, utilizes principal component method to extract face component feature；
In step S25, as result is mated, then prompting " successfully ", extracts face characteristic value simultaneously
It is saved in server user's face database；As result is not mated, then prompting " again ", again
Start photo acquisition.
A kind of user's on-line authentication system based on In vivo detection and recognition of face, described system includes:
User's online registration module, for the online fill message of user, and according to the ID (identity number) card No. submitted to
Transferred the Certification of Second Generation photo of correspondence by public security Intranet, extract photo face characteristic value, set up user
Face characteristic Value Data storehouse；
User's on-line authentication module, including In vivo detection unit, graphics processing unit, feature extraction list
Unit, face alignment unit, result treatment unit；
Described In vivo detection unit is in order to be confirmed whether being live body and the optimum human face photo of acquisition；Live body is examined
Survey unit and include that head rotation direction obtains subelement, position generates subelement, object of which movement drives
Subelement, head rotation direction obtain subelement in order to obtain the video in head rotation direction, and from
The rotation direction of middle acquisition head；Position generates the subelement position in order to stochastic generation object, with
And object needs the appointment position of arrival；Object of which movement drives subelement in order to according to head rotation side
To driving object of which movement, only reach to specify position by head rotation band animal body, be then judged as
Live body；Choose the minimum photo of the anglec of rotation for optimum human face photo simultaneously；Described In vivo detection list
Unit includes Face datection subelement and human face posture detection sub-unit；
Face datection subelement is in order to determine whether face and structures locating；To camera collection to every
One two field picture,
Carry out greyscale transformation, Filtering Processing, it is thus achieved that high-quality gray-scale map；Utilize integration quick gray-scale map
Calculate Harr-Like wavelet character value, be applied to the AdaBoost-Cascade that off-line training is good
Grader, it determines whether be face；Face shape facility according to face and
AdaBoost-Cascade grader carries out eyes, double eyebrow, nose, mouth to the region of face window
Bar and lower jaw location, determine human face position；
Human face posture detection sub-unit is in order to use based on oval template and the Attitude estimation of face position
Method carries out attitude detection；The position of eyes, nose and face is determined, to inspection by Face datection
The face connected domain border measured carries out ellipse fitting and obtains oval template, calculate eyes, face and
The location parameter in a template of nose, sends location parameter into what three-layer artificial neural network obtained
The rough estimate value of attitude parameter；For improving the precision of Attitude estimation, will according to rough estimate result
Input picture sends into corresponding linear correlation wave filter after treatment, obtains relatively accurate face
Attitude estimation result；
Described graphics processing unit carries out light compensation, ash in order to the human face photo collecting optimum attitude
Spend conversion, histogram equalization, normalization, geometric correction, filter and sharpen, serve feature
Extract；
Described feature extraction unit, in order to the human face photo processed, extracts face component feature, bag
Include naked face, eyebrow, eyes, mouth face component, utilize principal component method to extract face component
Feature；
Described face alignment unit is in order to the characteristic of the facial image of collection extracted and Certification of Second Generation
Human face data, by setting a threshold value, when similarity exceedes this threshold value, then obtains coupling
Result output；
Described result treatment unit is in order to make respective handling according to the comparison result of face alignment unit；
As result is mated, then prompting " certification success ", extract face characteristic value simultaneously and be saved in service
Device user's face database；As result is not mated, then prompting " re-authentication ", restarts to shine
Sheet gathers.
A kind of user's on-line authentication system based on In vivo detection and recognition of face, described system includes:
-user online registration module, for the online fill message of user, obtains corresponding face characteristic value,
And set up user's face characteristic Value Data storehouse；
-user on-line authentication module, including In vivo detection unit, graphics processing unit, feature extraction
Unit, face alignment unit, result treatment unit；
Described In vivo detection unit is in order to confirm whether certification user is live body and obtains human face photo；
Described graphics processing unit is in order to process the human face photo gathered；
Described feature extraction unit, in order to the human face photo processed, extracts face component feature；
Described face alignment unit is in order to the characteristic of the facial image of collection extracted and described use
Corresponding human face data in face characteristic Value Data storehouse, family, by setting a threshold value, works as similarity
Exceed this threshold value, then result coupling obtained exports；
Described result treatment unit is in order to make respective handling according to face alignment result.
As a preferred embodiment of the present invention, described user's online registration module is according to the identity submitted to
Card number transfers the Certification of Second Generation photo of correspondence by public security Intranet, extracts photo face characteristic value, builds
Vertical user's face characteristic Value Data storehouse.
As a preferred embodiment of the present invention, described In vivo detection unit is in order to be confirmed whether being live body
And obtain optimum human face photo；In vivo detection unit includes that head rotation direction obtains subelement, position
Put generation subelement, object of which movement drives subelement, and head rotation direction obtains subelement in order to obtain
Take the video in head rotation direction, and therefrom obtain the rotation direction of head；Position generates subelement
In order to the position of stochastic generation object, and object needs the appointment position of arrival；Object of which movement drives
Subunit, in order to drive object of which movement according to head rotation direction, is only driven by head rotation
Object reaches to specify position, then be judged as live body；Choose the minimum photo of the anglec of rotation is the most simultaneously
Excellent human face photo；Described In vivo detection unit includes Face datection subelement and human face posture detection
Unit；
Face datection subelement is in order to determine whether face and structures locating；To camera collection to every
One two field picture, carries out greyscale transformation, Filtering Processing, it is thus achieved that high-quality gray-scale map；Gray scale is desired to make money or profit
Quickly calculate Harr-Like wavelet character value with integration, be applied to off-line training good
AdaBoost-Cascade grader, it determines whether be face；Face shape facility according to face
With AdaBoost-Cascade grader the region of face window carried out eyes, double eyebrow, nose,
Face and lower jaw location, determine human face position；
Human face posture detection sub-unit is in order to use based on oval template and the Attitude estimation of face position
Method carries out attitude detection；The position of eyes, nose and face is determined, to inspection by Face datection
The face connected domain border measured carries out ellipse fitting and obtains oval template, calculate eyes, face and
The location parameter in a template of nose, sends location parameter into what three-layer artificial neural network obtained
The rough estimate value of attitude parameter；For improving the precision of Attitude estimation, will according to rough estimate result
Input picture sends into corresponding linear correlation wave filter after treatment, obtains relatively accurate face
Attitude estimation result.
As a preferred embodiment of the present invention, described graphics processing unit is in order to collect optimum attitude
Human face photo carry out light compensation, greyscale transformation, histogram equalization, normalization, geometry school
Just, filtering and sharpening, serving feature extraction；
Described feature extraction unit, in order to the human face photo processed, extracts face component feature, bag
Include naked face, eyebrow, eyes, mouth face component, utilize principal component method to extract face component
Feature；
Described face alignment unit is in order to the characteristic of the facial image of collection extracted and Certification of Second Generation
Human face data, by setting a threshold value, when similarity exceedes this threshold value, then obtains coupling
Result output；
Described result treatment unit is in order to make respective handling according to the comparison result of face alignment unit；
As result is mated, then prompting " certification success ", extract face characteristic value simultaneously and be saved in service
Device user's face database；As result is not mated, then prompting " re-authentication ", restarts to shine
Sheet gathers.
The beneficial effects of the present invention is: the present invention propose based on In vivo detection and the use of recognition of face
Family on-line authentication method and system, can avoid using the video containing face to gain certification by cheating, improve
Security of system.The present invention command operating by game type, it is ensured that the photo collected is user
Photo in person；Meanwhile, the eigenvalue of the human face photo of success identity is saved in user's face database,
So comparison of one-to-many is greatly shortened recognition time and improves recognition accuracy.
Accompanying drawing explanation
Fig. 1 is the flow chart of existing on-line authentication method.
Fig. 2 is the flow chart of on-line authentication method of the present invention.
Fig. 3 is the composition schematic diagram of on-line authentication system of the present invention.
Detailed description of the invention
Describe the preferred embodiments of the present invention below in conjunction with the accompanying drawings in detail.
Embodiment one
Refer to Fig. 1, present invention is disclosed a kind of user based on In vivo detection and recognition of face online
Authentication method, described method includes:
[step S10] user online registration step: the online fill message of user, and according to the body submitted to
Part card number transfers the Certification of Second Generation photo of correspondence by public security Intranet, extracts photo face characteristic value,
Set up user's face characteristic Value Data storehouse.
[step S20] user on-line authentication step (e.g., can be logged on certification)；Examine including live body
Survey step, image processing step, characteristic extraction step, face alignment step, result treatment step.
Specifically include:
-step S21, In vivo detection step, be confirmed whether it is live body and the optimum human face photo of acquisition；
First on setting screen, generate the video of bead motion, and record the rail of the bead each time point of motion
Mark；If it is identical with the combination of the bead direction of motion to collect the combination of continuous human face posture, then it is judged as living
Body, is otherwise photo or video；Choose the minimum photo of the anglec of rotation to shine for optimum face simultaneously
Sheet.Another kind of In vivo detection mode is: based on head rotation direction as the determining program instructed,
Only drive bead to reach to specify position by head rotation within the setting time, be then judged as living
Body, can authenticate.
In vivo detection step includes Face datection and human face posture detection；
In Face datection step, it is determined whether be face and structures locating；To camera collection to each
Two field picture, carries out greyscale transformation, Filtering Processing, it is thus achieved that high-quality gray-scale map；Gray-scale map is utilized
Integration quickly calculates Harr-Like wavelet character value, is applied to off-line training good
AdaBoost-Cascade grader, it determines whether be face；Face shape facility according to face
With AdaBoost-Cascade grader the region of face window carried out eyes, double eyebrow, nose,
Face and lower jaw location, determine human face position；
In human face posture detecting step, use based on oval template and the Attitude estimation side of face position
Method；First pass through Face datection and determine the position of eyes, nose and face, then to detecting
Face connected domain border carries out ellipse fitting and obtains oval template, calculates eyes, face and nose
Location parameter in a template, finally sends into the appearance that three-layer artificial neural network obtains by location parameter
The rough estimate value of state parameter；For improving the precision of Attitude estimation, according to rough estimate result by defeated
Enter image and send into corresponding linear correlation wave filter after certain process, obtain relatively accurate people
Face Attitude estimation result；
-step S22, image processing step；The human face photo collecting optimum attitude carries out light benefit
Repay, greyscale transformation, histogram equalization, normalization, geometric correction, filter and sharpen, service
In feature extraction；
-step S23, characteristic extraction step；To the human face photo processed, extract face component special
Levy, including naked face, eyebrow, eyes, mouth face component, utilize principal component method to extract people
Face component feature；
-step S24, face alignment step；The characteristic and two of the facial image of the collection extracted
Generation card human face data, by setting a threshold value, when similarity exceedes this threshold value, then coupling
The result output obtained；
-step S25, result treatment step；As result is mated, then prompting " certification success ", with
Time extract face characteristic value and be saved in server user's face database；As result is not mated, then
Prompting " re-authentication ", restarts photo acquisition.
It is described above present invention user based on In vivo detection and recognition of face on-line authentication method
Flow process, the present invention, while disclosing said method, also discloses a kind of based on In vivo detection and face
The user's on-line authentication system identified；Described system includes: user's online registration module, Yong Hu
Line authentication module.
User's online registration module is for the online fill message of user, and leads to according to the ID (identity number) card No. submitted to
Cross public security Intranet and transfer the Certification of Second Generation photo (can certainly be other human face photos) of correspondence, carry
Take photo face characteristic value, set up user's face characteristic Value Data storehouse.
User's on-line authentication module include In vivo detection unit, graphics processing unit, feature extraction unit,
Face alignment unit, result treatment unit.
Described In vivo detection unit is in order to be confirmed whether being live body and the optimum human face photo of acquisition；Live body is examined
Survey unit and include that object of which movement presents subelement, in order to generate regarding of bead motion on setting screen
Frequently, the track of the In vivo detection unit record bead each time point of motion, and change according to human face posture
Judge whether the human face photo gathered is live body photo；If collecting the combination of continuous human face posture with little
The combination of the ball direction of motion is identical, then be judged as live body, be otherwise photo or video；Choose simultaneously
The photo of anglec of rotation minimum is optimum human face photo.Further, it is also possible to detect by other means
Live body, as In vivo detection unit include head rotation direction obtain subelement, position generate subelement,
Object of which movement drives subelement, and head rotation direction obtains subelement in order to obtain head rotation direction
Video, and therefrom obtain the rotation direction of head；Position generates subelement in order to stochastic generation thing
The position of body, and the appointment position that object needs arrive；Object of which movement drives subelement in order to root
Drive object of which movement according to head rotation direction, only reach specific bit by head rotation band animal body
Put, be then judged as live body.
Described In vivo detection unit includes Face datection subelement and human face posture detection sub-unit.
Face datection subelement is in order to determine whether face and structures locating；To camera collection to every
One two field picture, carries out greyscale transformation, Filtering Processing, it is thus achieved that high-quality gray-scale map；Gray scale is desired to make money or profit
Quickly calculate Harr-Like wavelet character value with integration, be applied to off-line training good
AdaBoost-Cascade grader, it determines whether be face；Face shape facility according to face
With AdaBoost-Cascade grader the region of face window carried out eyes, double eyebrow, nose,
Face and lower jaw location, determine human face position.
Human face posture detection sub-unit is in order to use based on oval template and the Attitude estimation of face position
Method carries out attitude detection；The position of eyes, nose and face is determined, to inspection by Face datection
The face connected domain border measured carries out ellipse fitting and obtains oval template, calculate eyes, face and
The location parameter in a template of nose, sends location parameter into what three-layer artificial neural network obtained
The rough estimate value of attitude parameter；For improving the precision of Attitude estimation, will according to rough estimate result
Input picture sends into corresponding linear correlation wave filter after treatment, obtains relatively accurate face
Attitude estimation result.
Described graphics processing unit carries out light compensation, ash in order to the human face photo collecting optimum attitude
Spend conversion, histogram equalization, normalization, geometric correction, filter and sharpen, serve feature
Extract.
Described feature extraction unit, in order to the human face photo processed, extracts face component feature, bag
Include naked face, eyebrow, eyes, mouth face component, utilize principal component method to extract face component
Feature.
Described face alignment unit is in order to the characteristic of the facial image of collection extracted and Certification of Second Generation
Human face data, by setting a threshold value, when similarity exceedes this threshold value, then obtains coupling
Result output.
Described result treatment unit is in order to make respective handling according to the comparison result of face alignment unit；
As result is mated, then prompting " certification success ", extract face characteristic value simultaneously and be saved in service
Device user's face database；As result is not mated, then prompting " re-authentication ", restarts to shine
Sheet gathers.
In sum, the user on-line authentication side based on In vivo detection and recognition of face that the present invention proposes
Method and system, can avoid using the video containing face to gain certification by cheating, improve security of system.
The present invention command operating by game type, it is ensured that the photo collected is user's photo；With
Time, the eigenvalue of the human face photo of success identity is saved in user's face database, such one-to-many
Comparison be greatly shortened recognition time and improve recognition accuracy.
Here description of the invention and application is illustrative, is not wishing to limit the scope of the invention
In the above-described embodiments.The deformation of embodiments disclosed herein and change are possible, for
For those skilled in the art, the various parts with equivalence of replacing of embodiment are public
Know.It should be appreciated by the person skilled in the art that without departing from the spirit of the present invention or essence
In the case of feature, the present invention can in other forms, structure, layout, ratio, Yi Jiyong
Other assembly, material and parts realize.In the case of without departing from scope and spirit of the present invention,
Embodiments disclosed herein can be carried out other deformation and change.
Claims (8)
1. user's on-line authentication method based on In vivo detection and recognition of face, it is characterised in that described
Method includes:
Step S10, user's online registration step: the online fill message of user, and according to the identity card submitted to
Number transfers the Certification of Second Generation photo of correspondence by public security Intranet, extracts photo face characteristic value, sets up user
Face characteristic Value Data storehouse；
Step S20, user's on-line authentication step；Including In vivo detection step, image processing step, feature
Extraction step, face alignment step, result treatment step；Specifically include:
-step S21, In vivo detection step, be confirmed whether it is live body and the optimum human face photo of acquisition；Based on head
Portion's rotation direction, as the determining program of instruction, only drives bead by head rotation within the setting time
Reach to specify position, be then judged as live body, can authenticate；Choose attitude optimum human face photo simultaneously；Live
Body detecting step includes Face datection and human face posture detection；
In Face datection step, it is determined whether be face and structures locating；To camera collection to each frame figure
Picture, carries out greyscale transformation, Filtering Processing, it is thus achieved that high-quality gray-scale map；Utilize integration quick gray-scale map
Calculate Harr-Like wavelet character value, be applied to the AdaBoost-Cascade classification that off-line training is good
Device, it determines whether be face；Face shape facility according to face and AdaBoost-Cascade grader
The region of face window is carried out eyes, double eyebrow, nose, face and lower jaw location, determines human face
Position；
In human face posture detecting step, use based on oval template and the Attitude estimation method of face position；First
First pass through Face datection and determine the position of eyes, nose and face, then to the face connected domain detected
Border carries out ellipse fitting and obtains oval template, calculates eyes, face and the position in a template of nose
Parameter, finally sends into the rough estimate value of the attitude parameter that three-layer artificial neural network obtains by location parameter；
For improving the precision of Attitude estimation, according to rough estimate result, input picture sent into after treatment neighborhood
Weighting filter, obtains relatively accurate human face modeling result；
-step S22, image processing step；The human face photo collecting optimum attitude carries out light compensation, ash
Spend conversion, histogram equalization, normalization, geometric correction, filter and sharpen, serve feature extraction；
-step S23, characteristic extraction step；To the human face photo processed, extract face component feature, bag
Include naked face, eyebrow, eyes, mouth face component, utilize principal component method to extract face component feature；
-step S24, face alignment step；The characteristic of the facial image of the collection extracted and secondary witness
Face data, by setting a threshold value, when similarity exceedes this threshold value, then result coupling obtained
Output；
-step S25, result treatment step；As result is mated, then prompting " certification success ", extract simultaneously
It is saved in server user's face database to face characteristic value；As result is not mated, then prompting is " again
Certification ", restart photo acquisition.
2. user's on-line authentication method based on In vivo detection and recognition of face, it is characterised in that described
Method includes:
Step S10, user's online registration step: the online fill message of user, obtain corresponding face characteristic value,
Set up user's face characteristic Value Data storehouse；
Step S20, user's on-line authentication step；Including In vivo detection step, image processing step, feature
Extraction step, face alignment step, result treatment step；Specifically include:
-step S21, In vivo detection step, confirm whether certification user is live body and obtains human face photo；
In vivo detection step in described step S21 includes Face datection and human face posture detection；
In Face datection step, it is determined whether be face and structures locating；To camera collection to each frame figure
Picture, carries out greyscale transformation, Filtering Processing, it is thus achieved that high-quality gray-scale map；
Gray-scale map utilizes integration quickly calculate Harr-Like wavelet character value, is applied to off-line training good
AdaBoost-Cascade grader, it determines whether be face；
Face shape facility according to face and the AdaBoost-Cascade grader region to face window
Carry out eyes, double eyebrow, nose, face and lower jaw location, determine human face position；
In human face posture detecting step, use based on oval template and the Attitude estimation method of face position；
First pass through Face datection and determine the position of eyes, nose and face, then to the face detected even
Logical border, territory carries out ellipse fitting and obtains oval template, calculate eyes, face and nose in a template
Location parameter, finally sends into estimating roughly of the attitude parameter that three-layer artificial neural network obtains by location parameter
Evaluation；
For improving the precision of Attitude estimation, according to rough estimate result, input picture sent into after treatment phase
The linear correlation wave filter answered, obtains relatively accurate human face modeling result；
-step S22, image processing step；The human face photo gathered is processed；
-step S23, characteristic extraction step；To the human face photo processed, extract face component feature；
-step S24, face alignment step；The characteristic of the facial image of the collection extracted and described user
Corresponding human face data in face characteristic Value Data storehouse, by setting a threshold value, when similarity exceedes this
One threshold value, then result coupling obtained exports；
-step S25, result treatment step；Respective handling is made according to face alignment result.
User's on-line authentication method based on In vivo detection and recognition of face the most according to claim 2,
It is characterized in that:
In described step S21, it may be judged whether the method for live body is: based on head rotation direction as instruction
Determining program, only by head rotation band animal body reach specify position, then be judged as live body；With
Time choose the minimum photo of the anglec of rotation for optimum human face photo.
User's on-line authentication method based on In vivo detection and recognition of face the most according to claim 2,
It is characterized in that:
In step S22, the human face photo collecting optimum attitude carries out light compensation, greyscale transformation, Nogata
Figure equalization, normalization, geometric correction, filter and sharpen, serve feature extraction；
In step S23, the face component feature of extraction includes naked face, eyebrow, eyes, mouth face component,
Principal component method is utilized to extract face component feature；
In step S25, as result is mated, then prompting " certification success ", extract face characteristic value simultaneously
It is saved in server user's face database；As result is not mated, then prompting " re-authentication ", again
Start photo acquisition.
5. user's on-line authentication system based on In vivo detection and recognition of face, it is characterised in that described
System includes:
User's online registration module, for the online fill message of user, and passes through according to the ID (identity number) card No. submitted to
Public security Intranet transfers the Certification of Second Generation photo of correspondence, extracts photo face characteristic value, sets up user's face characteristic
Value Data storehouse；
User's on-line authentication module, including In vivo detection unit, graphics processing unit, feature extraction unit,
Face alignment unit, result treatment unit；
Described In vivo detection unit is in order to be confirmed whether being live body and the optimum human face photo of acquisition；In vivo detection list
Unit includes that head rotation direction obtains subelement, position generates subelement, object of which movement drives subelement,
Head rotation direction acquisition subelement is in order to obtain the video in head rotation direction, and therefrom obtains head
Rotation direction；Position generates the subelement position in order to stochastic generation object, and object needs arrival
Specify position；Object of which movement drives subelement in order to drive object of which movement according to head rotation direction, only
Reach to specify position by head rotation band animal body, be then judged as live body；Choose the anglec of rotation the most simultaneously
Little photo is optimum human face photo；Described In vivo detection unit includes Face datection subelement and face appearance
State detection sub-unit；
Face datection subelement is in order to determine whether face and structures locating；To camera collection to each frame
Image, carries out greyscale transformation, Filtering Processing, it is thus achieved that high-quality gray-scale map；Utilize integration fast gray-scale map
Speed calculates Harr-Like wavelet character value, and the AdaBoost-Cascade being applied to off-line training good divides
Class device, it determines whether be face；Face shape facility according to face and AdaBoost-Cascade classification
Device carries out eyes, double eyebrow, nose, face and lower jaw location to the region of face window, determines face device
Official position；
Human face posture detection sub-unit is in order to use based on oval template and the Attitude estimation method of face position
Carry out attitude detection；The position of eyes, nose and face is determined, to the people detected by Face datection
Face connected domain border carries out ellipse fitting and obtains oval template, calculate eyes, face and nose in template
In location parameter, location parameter is sent into estimating roughly of the attitude parameter that three-layer artificial neural network obtains
Evaluation；For improving the precision of Attitude estimation, according to rough estimate result, input picture is sent after treatment
Enter corresponding linear correlation wave filter, obtain relatively accurate human face modeling result；
Described graphics processing unit carries out light compensation in order to the human face photo collecting optimum attitude, gray scale becomes
Change, histogram equalization, normalization, geometric correction, filter and sharpen, serve feature extraction；
Described feature extraction unit, in order to the human face photo processed, extracts face component feature, including naked
Face, eyebrow, eyes, mouth face component, utilize principal component method to extract face component feature；
Described face alignment unit is in order to the characteristic of the facial image of collection extracted and Certification of Second Generation face
Data, by setting a threshold value, when similarity exceedes this threshold value, then defeated for the result that coupling obtains
Go out；
Described result treatment unit is in order to make respective handling according to the comparison result of face alignment unit；Such as knot
Fruit coupling, then prompting " certification success ", extract face characteristic value simultaneously and be saved in server user people
Face data base；As result is not mated, then prompting " re-authentication ", restarts photo acquisition.
6. user's on-line authentication system based on In vivo detection and recognition of face, it is characterised in that described
System includes:
-user online registration module, for the online fill message of user, obtains corresponding face characteristic value, and builds
Vertical user's face characteristic Value Data storehouse；
-user on-line authentication module, including In vivo detection unit, graphics processing unit, feature extraction unit,
Face alignment unit, result treatment unit；
Described In vivo detection unit is in order to confirm whether certification user is live body and obtains human face photo；Live body is examined
Survey unit and include that head rotation direction obtains subelement, position generates subelement, object of which movement drives son single
Unit, head rotation direction acquisition subelement is in order to obtain the video in head rotation direction, and therefrom obtains head
The rotation direction in portion；Position generates the subelement position in order to stochastic generation object, and object needs
The appointment position reached；Object of which movement drive subelement in order to according to head rotation direction drive object of which movement,
Only reach to specify position by head rotation band animal body, be then judged as live body；Choose the anglec of rotation simultaneously
The photo of degree minimum is optimum human face photo；Described In vivo detection unit includes Face datection subelement and people
Face attitude detection subelement；
Face datection subelement is in order to determine whether face and structures locating；To camera collection to each frame
Image, carries out greyscale transformation, Filtering Processing, it is thus achieved that high-quality gray-scale map；Utilize integration fast gray-scale map
Speed calculates Harr-Like wavelet character value, and the AdaBoost-Cascade being applied to off-line training good divides
Class device, it determines whether be face；Face shape facility according to face and AdaBoost-Cascade classification
Device carries out eyes, double eyebrow, nose, face and lower jaw location to the region of face window, determines face device
Official position；
Human face posture detection sub-unit is in order to use based on oval template and the Attitude estimation method of face position
Carry out attitude detection；The position of eyes, nose and face is determined, to the people detected by Face datection
Face connected domain border carries out ellipse fitting and obtains oval template, calculate eyes, face and nose in template
In location parameter, location parameter is sent into estimating roughly of the attitude parameter that three-layer artificial neural network obtains
Evaluation；For improving the precision of Attitude estimation, according to rough estimate result, input picture is sent after treatment
Enter corresponding linear correlation wave filter, obtain relatively accurate human face modeling result；
Described graphics processing unit is in order to process the human face photo gathered；
Described feature extraction unit, in order to the human face photo processed, extracts face component feature；
Described face alignment unit is in order to the characteristic of the facial image of collection extracted and described user people
Corresponding human face data in face characteristic value data storehouse, by setting a threshold value, when similarity exceedes this
Threshold value, then result coupling obtained exports；
Described result treatment unit is in order to make respective handling according to face alignment result.
User's on-line authentication system based on In vivo detection and recognition of face the most according to claim 6,
It is characterized in that:
Described user's online registration module transfers correspondence according to the ID (identity number) card No. submitted to by public security Intranet
Certification of Second Generation photo, extracts photo face characteristic value, sets up user's face characteristic Value Data storehouse.
User's on-line authentication system based on In vivo detection and recognition of face the most according to claim 6,
It is characterized in that:
Described graphics processing unit carries out light compensation in order to the human face photo collecting optimum attitude, gray scale becomes
Change, histogram equalization, normalization, geometric correction, filter and sharpen, serve feature extraction；
Described feature extraction unit, in order to the human face photo processed, extracts face component feature, including naked
Face, eyebrow, eyes, mouth face component, utilize principal component method to extract face component feature；
Described face alignment unit is in order to the characteristic of the facial image of collection extracted and described user people
Corresponding human face data in face characteristic value data storehouse, by setting a threshold value, when similarity exceedes this
Threshold value, then result coupling obtained exports；
Described result treatment unit is in order to make respective handling according to the comparison result of face alignment unit；Such as knot
Fruit coupling, then prompting " certification success ", extract face characteristic value simultaneously and be saved in server user people
Face data base；As result is not mated, then prompting " re-authentication ", restarts photo acquisition.
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| CN110188684A (en) * | 2019-05-30 | 2019-08-30 | 湖南城市学院 | A kind of face identification device and method |
| CN110188684B (en) * | 2019-05-30 | 2021-04-06 | 湖南城市学院 | Face recognition device and method |
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