CN101923641A - Improved human face recognition method - Google Patents
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- CN101923641A CN101923641A CN2010102766799A CN201010276679A CN101923641A CN 101923641 A CN101923641 A CN 101923641A CN 2010102766799 A CN2010102766799 A CN 2010102766799A CN 201010276679 A CN201010276679 A CN 201010276679A CN 101923641 A CN101923641 A CN 101923641A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
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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
- G06V40/40—Spoof detection, e.g. liveness detection
- G06V40/45—Detection of the body part being alive
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Abstract
The invention discloses an improved human face recognition method, which belongs to the technical field of human face recognition. The improved human face recognition method comprises the following steps of: processing two or more acquired human face images, extracting a plurality of 2D characteristic points of the human face areas of the images, and establishing a corresponding relation of the human face 2D characteristic points of different images; judging whether the extracted human face 2D characteristic points belong to the same space plane or not according to the restriction conditions which the projections of the points of the same space plane on different images meet in a 3D space; and accordingly, judging whether the acquired images are plane scenes or not so as to determine whether the human faces are from other 2D pictures or not and prevent a human face recognition system from being deceived by other pictures. The method solves the problem that the traditional human face recognition system is easily deceived by the pictures, and improves the reliability of the human face recognition system by determining whether an input image is from a real human face or a picture.
Description
Technical field
The present invention relates to 2D graphical analysis and treatment technology in the biometrics identification technology, extract minutiae from two width of cloth or several 2D images specifically, and judge whether it belongs to the method for the same space planar point.
Background technology
Face recognition technology is a kind of in the biometrics identification technology.Each country all takes much count of face recognition technology at present, and many major companies have also released the identity identifying technology based on recognition of face, have wide practical use in fields such as video monitoring, multimedia, process control, identifications.Along with this The Application of Technology increases gradually, along with and shortcoming on some recognition technologies of coming also is utilized.Traditional face recognition technology based on feature by the unique point of extraction human face region, and according to certain the intrinsic relation between the special unique point, is discerned.Its basis is the relation between the unique point on the image, and has ignored the space attribute of unique point.This system is easy to be cheated by photo in application, and its reliability and security are received and had a strong impact on.Outstanding feature of the present invention is, in the conplane image that under different points of view, obtains in the space, should satisfy the isoplanar constraint, adopt stable operator, as LMEDS, calculate the plane homography matrix reliably according to character pair point, and based on this, carry out the isoplanar characteristic of unique point and differentiate.
Summary of the invention
The object of the present invention is to provide, utilization is estimated the isoplanar attribute of two-dimensional images unique point, whether the area of space with the judging characteristic point correspondence is same plane, judge that reliably whether image is from photo, but not actual living body faces, and then the technology of raising people face detecting reliability.
In order to achieve the above object, technical solution of the present invention provides a kind of improved face identification method, it is characterized in that, it is single because of the matrix constraint to satisfy the plane according to the corresponding point of isoplanar, space o'clock on two width of cloth planes of delineation, the unique point of the human face region that extracts is carried out the isoplanar constraint differentiate, to determine whether facial image obtains from plane picture.
Described method, it comprises the following steps:
Step 1 is obtained the image of two width of cloth or several same people's faces by shooting;
Step 2 is carried out feature extraction to the image that obtains and is handled, and obtains a plurality of 2D unique points, utilizes the vision matching technology to set up the corresponding relation of unique point;
Step 3, the character pair point on arbitrary extracting two width of cloth images calculates the plane homography matrix; The error amount of the unique point that the plane homography matrix calculation procedure 2 that utilization calculates is obtained;
Step 4, set an error higher limit, if the error amount of the unique point that step 3 calculates is smaller or equal to the error higher limit, then the explanation unique point that participates in calculating is positioned at same plane, if the error amount of unique point is greater than the error higher limit of setting then change step 5 over to;
Step 5, determine human face region in the image by method for detecting human face, unique point in the human face region is used to recomputate the plane homography matrix, and the error amount of the interior unique point of calculating human face region, if error amount is then judged unique point and belonged to same plane smaller or equal to the error amount of setting, the spatial point of human face region correspondence is the plane, if error amount is greater than the error amount of setting, then judging the image that photographs is real facial image.
The described image of step 1 is same people's face imaging to be obtained under diverse location by single camera, or takes acquisition by two or more video cameras at synchronization, or the fixing image to mobile people's face acquisition time of video camera
The single calculating because of matrix in above-mentioned plane adopts least square method to estimate or the LMEDS algorithm.
The advantage of the more existing face recognition technology of the present invention is:
When carrying out recognition of face, space plane attribute to the unique point that is used to discern is differentiated, determine whether these unique points belong to same plane, screen picture and actual facial image with this, thereby avoid being cheated, improved security and reliability during this type systematic uses by photograph image.The method that the present invention proposes is directly handled two dimensional image, scene is not set the constraint condition of priori, has more ubiquity.
Description of drawings
Fig. 1 is the face identification method process flow diagram that the present invention proposes.
Embodiment
The invention will be further described below in conjunction with drawings and Examples.
As shown in Figure 1, a kind of improved face identification method may further comprise the steps:
1). obtain the image of two width of cloth or several people's faces;
2). the image that obtains is carried out feature point extraction handle, obtain a plurality of 2D unique points, utilize the vision matching technology to set up the corresponding relation of unique point;
3). the character pair point on arbitrary extracting two width of cloth images, calculate plane homography matrix H;
4). calculate the error amount of all character pair points;
5). set an error higher limit, if calculate error amount in the step 4 smaller or equal to setting value, then unique point that participate in to calculate of explanation belongs to the isoplanar point, if the error amount of unique point greater than setting value change over to step next step;
6). determine the unique point of people's face surveyed area with method for detecting human face, and utilize unique point to calculate the plane homography matrix;
7). calculate the corresponding error of face characteristic provincial characteristics point;
8). set an error higher limit, whether the error that determining step 7 draws is smaller or equal to this setting value;
9). if then unique point belongs to the isoplanar point, if otherwise unique point does not belong to the isoplanar point, and be real facial image.
Principle of the present invention is: same video camera carries out imaging to same people's face under two diverse locations, perhaps two video cameras are simultaneously to the imaging of people's face, perhaps video camera is fixing to mobile people's face acquisition time image, principle is the same, belong to the point in the same space plane in the scene, coordinate position in two width of cloth images should satisfy the plane list because of the matrix constraint, that is:
Here
,
Be on two width of cloth images, the image coordinate that the same space point is corresponding,
HBe the plane homography matrix.
In the space, three points on same straight line can uniquely not determine a plane, and space plane can be used parameter
Describe, wherein n is the normal vector of space plane, and d is that space plane is the distance of round dot to video camera.This space plane parameter can calculate with linear method with three unique points.
On the plane of delineation, defining point to the distance on plane is:
When detecting, at first the image that obtains is extracted the 2D unique point, and set up the corresponding relation between the feature on the plane.
To feature of interest point on the plane of delineation and the corresponding point in other image thereof,, calculate the right distance error value of each point according to the range formula of putting the plane.If error amount all is not more than the error higher limit of a setting, the spatial point that these unique point correspondences then are described is the point on the same plane, concludes that thus the image that is obtained is from 2D plane facial images such as pictures.
Have greater than the error amount of setting if calculate the error amount of unique point this moment, then determine human face region characteristic of correspondence point, and utilize the unique point in the human face region to recomputate the plane homography matrix with method for detecting human face; Calculate the error of unique point correspondence in the human face region then; Set an error higher limit, whether the error of unique point is smaller or equal to this setting value in the determining step human face region.Belong to the isoplanar point if the unique point of human face region then is described, the image that is obtained is to be obtained by picture etc.; If otherwise unique point does not belong to the isoplanar point, can conclude that then the image that obtains is from real people's face.
Calculate the plane homography matrix for matching characteristic point, can adopt LMEDS to stablize method of estimation to improve the robustness of result of calculation.
In general, the location of unique point is noisy in the image characteristics extraction process, in addition, the correspondence of unique point is set up process, may produce wrong factors such as match point, the present invention has reduced the influence of these factors to the plane testing result by adopting the robust method for parameter estimation, makes the result more reliable.
One, one video camera of embodiment is fixed on the support.During experiment, the people is positioned at suitable position, video camera the place ahead, because human body has light exercise, two width of cloth facial images of inscribing when video camera obtains two respectively.
Two width of cloth images that obtain are carried out feature point extraction handle, establish in first width of cloth image, the unique point of extraction is
p i (
i=1,2,3 ..., n), in second width of cloth image, the unique point of obtaining is
p ' i (
i=1,2,3 ..., m).Utilize the stereoscopic vision matching technique, set up the corresponding relation of these unique points:
According to the isoplanar equation of constraint,
, adopt least square method to estimate to obtain the value of plane homography matrix H.
Calculate the error amount of each character pair point:
If
In the formula,
Be the error amount of setting.Think that then these unique points are corresponding to same space plane.
All smaller or equal to setting value, then utilize human face detection tech in two width of cloth images, to detect human face region if not the error of all unique points, the unique point that belongs to human face region in the matching characteristic point that has obtained is screened.Only the character pair point with human face region recomputates plane homography matrix H
f, and calculate the error amount of human face region character pair point, set an error higher limit
If:
In the formula, m thinks then that for the corresponding matching characteristic point number that belongs to human face region these unique points of human face region are corresponding to same space plane.Otherwise think that the image that is obtained comes from real people's face.
Embodiment two, and obtaining of facial image is that two video cameras have a certain degree, and takes same individual face simultaneously.To the image extract minutiae taken and set up corresponding relation, utilize the LMEDS method of good reliability to estimate to obtain the H matrix.Calculate each unique point error amount and with the error higher limit of setting relatively, if be not more than setting value then unique point belong to the isoplanar point, if greater than setting value, then utilize human face detection tech to detect human face region, the unique point of extracting in the human face region recomputates the plane list because of matrix, calculate the error amount of human face region unique point and compare with the error amount of setting, if then think these points of human face region corresponding to same plane smaller or equal to setting value, if greater than setting value then the image that obtains of decidable from real people's face.
The present invention can realize stablizing, carrying out reliably the plane attribute differentiation of unique point, can prevent to be cheated by the input picture in 2D images such as photo source in the recognition of face.
Claims (3)
1. an improved face identification method is characterized in that, comprises following key step:
Step 1 is obtained the image of two width of cloth or several same people's faces by shooting;
Step 2 is carried out feature extraction to the image that obtains and is handled, and obtains a plurality of 2D unique points, utilizes the vision matching technology to set up the corresponding relation of unique point;
Step 3, the character pair point on arbitrary extracting two width of cloth images calculates the plane homography matrix; The error amount of the unique point that the plane homography matrix calculation procedure 2 that utilization calculates is obtained;
Step 4, set an error higher limit, if the error amount of the unique point that step 3 calculates is smaller or equal to the error higher limit, then the explanation unique point that participates in calculating is positioned at same plane, if the error amount of unique point is greater than the error higher limit of setting then change step 5 over to;
Step 5, determine human face region in the image by method for detecting human face, unique point in the human face region is used to recomputate the plane homography matrix, and the error amount of the interior unique point of calculating human face region, if error amount is then judged unique point and belonged to same plane smaller or equal to the error amount of setting, the spatial point of human face region correspondence is the plane, if error amount is greater than the error amount of setting, then judging the image that photographs is real facial image.
2. face identification method according to claim 1, it is characterized in that: the described image of step 1 is same people's face imaging to be obtained under diverse location by single camera, or take to obtain at synchronization, or the fixing image of video camera to people's face acquisition time of moving by two or more video cameras.
3. face identification method according to claim 1 is characterized in that: the single calculating because of matrix in described plane adopts least square method to estimate or the LMEDS algorithm.
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102194106A (en) * | 2011-05-11 | 2011-09-21 | 西安理工大学 | Human face recognition method used in door access system |
| CN103051887A (en) * | 2013-01-23 | 2013-04-17 | 河海大学常州校区 | Eagle eye-imitated intelligent visual sensing node and work method thereof |
| CN103106703A (en) * | 2013-01-14 | 2013-05-15 | 张平 | Anti-cheating driver training recorder |
| CN103971132A (en) * | 2014-05-27 | 2014-08-06 | 重庆大学 | Method for face recognition by adopting two-dimensional non-negative sparse partial least squares |
| CN104217504A (en) * | 2014-08-26 | 2014-12-17 | 杭州摩科商用设备有限公司 | Identity recognition self-service terminal and corresponding certificate of house property printing terminal |
| CN104217503A (en) * | 2014-08-26 | 2014-12-17 | 杭州摩科商用设备有限公司 | Self-service terminal identity identification method and corresponding house property certificate printing method |
| CN104737179A (en) * | 2012-10-18 | 2015-06-24 | 摩福公司 | Method for authenticating an image capture of a three-dimensional entity |
| CN105868733A (en) * | 2016-04-21 | 2016-08-17 | 腾讯科技（深圳）有限公司 | Face in-vivo validation method and device |
| CN106557723A (en) * | 2015-09-25 | 2017-04-05 | 北京市商汤科技开发有限公司 | A kind of system for face identity authentication with interactive In vivo detection and its method |
| CN106557726A (en) * | 2015-09-25 | 2017-04-05 | 北京市商汤科技开发有限公司 | A kind of band is mourned in silence the system for face identity authentication and its method of formula In vivo detection |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060285755A1 (en) * | 2005-06-16 | 2006-12-21 | Strider Labs, Inc. | System and method for recognition in 2D images using 3D class models |
| CN101196986A (en) * | 2007-12-25 | 2008-06-11 | 哈尔滨工业大学 | Three-dimensional palm print identity identifier and its identification method |
| US7403658B2 (en) * | 2005-04-15 | 2008-07-22 | Microsoft Corporation | Direct homography computation by local linearization |
-
2010
- 2010-09-09 CN CN2010102766799A patent/CN101923641B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7403658B2 (en) * | 2005-04-15 | 2008-07-22 | Microsoft Corporation | Direct homography computation by local linearization |
| US20060285755A1 (en) * | 2005-06-16 | 2006-12-21 | Strider Labs, Inc. | System and method for recognition in 2D images using 3D class models |
| CN101196986A (en) * | 2007-12-25 | 2008-06-11 | 哈尔滨工业大学 | Three-dimensional palm print identity identifier and its identification method |
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| CN104737179B (en) * | 2012-10-18 | 2019-05-14 | 摩福公司 | Method for authenticating the picture catching of 3D solid |
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| CN103971132A (en) * | 2014-05-27 | 2014-08-06 | 重庆大学 | Method for face recognition by adopting two-dimensional non-negative sparse partial least squares |
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