CN102013011B - Front-face-compensation-operator-based multi-pose human face recognition method - Google Patents
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Abstract
The invention discloses a simple and effective principal component analysis (PCA) algorithm-based multi-pose human face recognition technique, which compensating a multi-pose human face by using a front face compensation operator and using the compensated human face for multi-pose human face recognition. The attached figure in the abstract of the description is the whole multi-pose human face recognition flow chart. In the invention, when the PCA algorithm is used for human face recognition, the front face compensation operator compensates for front face profile information, namely feature face information corresponding to the large feature value resolved by the PCA algorithm, lacking in a multi-pose human face to be recognized, and reduces part of multi-pose human face information interfering with the PCA algorithm. A multi-pose human face usually lacks a front face profile which is more important information for PCA algorithm. Compared with the prior art, the technique has the advantages that: calculating by using an average face and avoiding using the method for training by forming a large matrix with human faces from a human face library, the calculation amount is reduced; thehuman face normalization requirement is low; it is easy to choose a human face library; and fewer faces are required to be trained. In addition, the algorithm used by the technique is simple, a good recognition effect can be achieved by simple addition and subtraction operation, and the technique can be used for recognizing human faces in all poses. In the invention, the method for compensating the multi-pose human face by using the front face compensation operator provides a new way for improving multi-pose human face recognition rate.
Description
Technical field
The present invention relates to a kind of colourful attitude face recognition technology based on PCA (principal component analysis (PCA)), more specifically, relate to and a kind ofly compensate people's face under the various attitude conditions by positive face compensating operator, the method for identifying by the people's face behind the using compensation.
Background technology
It is many that ground was studied in colourful attitude recognition of face in recent years, and it b referred to as computer vision and area of pattern recognition better one of major issue of solution not as yet.People's face is subject to multiple factor affecting, as expression, beard, glasses, hair, illumination, background, and the deflection of people's face causes only seeing the situation of side face, and the method that proposes at the hot issue of colourful attitude recognition of face roughly can be divided three classes: conventional method, three-dimensional research method, two-dimentional research method.
Conventional algorithm mainly solves the problem that is caused by variations such as illumination, expression, ages, and the variation research of the facial image that causes for the difference of observation visual angle is less.Three-dimensional research method generally is to form human face three-dimensional model from angle acquisition people face as much as possible, and uses three-dimensional model to make up face database, and people's face to be identified and these three-dimensional models mate to identify one by one.The problem of three-dimensional research method is that calculated amount is big, needs huge storage space, and current face database can not satisfy its needs, so it moves towards practicability and needs the long period.Mostly the two dimension research method is to seek each more colourful attitude people's face to the transformation relation of positive face, and people's face to be identified is changed into positive face identify, its theoretical foundation is that the difference between the identical attitude people's face of facial image diversity ratio different people of the different attitudes of same individual also wants big.The two dimension research method is compared with three-dimensional research method, and its advantage is that computation complexity is low, and computing velocity is fast, and memory capacity is little.
But two-dimentional research method also has significant disadvantages, is mainly reflected in four aspects:
1. the normalization of people's face is required height, and various recognizer is used different normalization modes.Any colourful attitude face recognition algorithms has people's face normalization requirement, yet because the shape difference between the different people face is bigger, therefore various recognizers can only be used different people's face method for normalizing according to concrete recognizer.
2. fuzzy to the requirement of face database.The training of human face that most of colourful attitude face recognition algorithms are used is selection after repeatedly attempting all, and when testing this algorithm again, difficulty finds on all four face database and to the processing mode of face database, the algorithm repetitive operation is poor.
3. two dimension research algorithm takes to train positive face and colourful attitude people's face to constitute a huge positive face matrix respectively all and side face matrix carries out computing usually, and calculated amount is big, and the processing time is long.
4. most two-dimentional algorithms are cut into the very little scope that only contains eyes, nose and mouth with people's face, have removed the information that much can distinguish people's face identity.But in the actual environment that uses general camera to monitor, through people's face pixel after shearing so seldom, almost lose face characteristic, can not identify.
Being used maximum in the face recognition algorithms is the PCA algorithm, and it has become a kind of normal data handling implement in fields such as Neuscience and Computer Image Processing.Face identification method based on PCA is regarded people's face as an integral body, utilizes the method for statistical learning to obtain eigenwert automatically, the simple and complete printenv restriction of its algorithm.Use the PCA algorithm, only need simple computation just the data dimensionality reduction of complexity can be simplified, though much obtained effect preferably based on the improvement algorithm of PCA, but increased the complexity of algorithm, versatility is low.
The PCA face recognition algorithms by a special eigenvectors matrix, projects to the vector of a higher-dimension in the vector space of a low-dimensional, is expressed as a low dimensional vector, can't lose useful information.Facial image is carried out after the PCA conversion, obtain eigenwert and the proper vector of people's face, wherein bigger eigenwert characteristic of correspondence vector has the shape with human face similarity, so be called eigenface again, utilize these eigenface can describe, express and approach facial image as substrate, carry out recognition of face.Figure of description 1 (a) has provided and has used 68 positive faces in the CMU PIE face database to carry out after the PCA conversion, maximum preceding 10 eigenwert characteristic of correspondence faces, Figure of description 1 (b) is 10 minimum eigenwert characteristic of correspondence faces, and eigenface is all by the descending arrangement of eigenwert among two figure.Eigenwert is more big, and the characteristic of correspondence face is more important, and the eigenface that eigenwert is big comprises more positive face profile information, and the little eigenface of eigenwert to comprise more be detailed information.The process of recognition of face is exactly that facial image to be identified is mapped on the subspace that eigenface launches, relatively itself and the distance of known person face in feature space, thus differentiate.
Patent CN101515324A discloses a kind of recognition of face that is applicable to multiple attitude system and method for deploying to ensure effective monitoring and control of illegal activities.This scheme utilizes the recognition of face sorter of corresponding attitude to extract face characteristic, and the face characteristic of corresponding attitude in recognition of face the sorter face characteristic that extracts and the storehouse of deploying to ensure effective monitoring and control of illegal activities is done contrast and judge whether to report to the police.This invention personnel selection face three-dimensional reconstruction technology goes out people's face of various attitudes with the object conversion of deploying to ensure effective monitoring and control of illegal activities on backstage, simultaneously with the recognition of face sorter extraction feature of corresponding attitude, and the algorithm complexity, and clearly do not mention people's face normalization problem.
Patent CN101458763A has announced a kind of automatic human face identification method based on image weighting average.This scheme at first utilizes the fast linear interpolation algorithm that original image is mapped to standard front face template, automatically differentiate the left and right sides deflection angle of people's face in the image then, and give every corresponding weights of image according to this angle and average, at last weighted average construction figure is obtained weighted average face image to weighted mean shape figure mapping.The colourful attitude image of people's face left and right sides deflection is only considered in this invention, and utilizing the fast linear interpolation algorithm that original image is mapped to this process preparation of standard front face template can not guarantee, and needs 34 unique points of mark, complex disposal process on the original image.
Patent CN101320484A discloses a kind of three-dimensional face identification method based on the full-automatic location of people's face.This scheme is at first set up two-dimension human face shape and local texture model, and two-dimension human face image is accurately located, and according to positioning result, described two-dimension human face image is carried out three-dimensional reconstruction, obtains three-dimensional face images.Then, described three-dimensional face images is carried out illumination model handle, obtain the virtual images of attitude, illumination variation.This invention calculated amount is big, and algorithm complexity, the effect behind the three-dimensional reconstruction are influenced greatly by the facial image fitness.
Summary of the invention
The objective of the invention is at the big problem of current colourful attitude recognition of face difficulty, a kind of simple effective colourful attitude face recognition technology based on PCA is provided, compensate colourful attitude people's face by positive face compensating operator, the method that the people's face after the using compensation is identified.Figure of description 2 is colourful attitude recognition of face process flow diagram of the present invention.
Colourful attitude face identification method based on positive face backoff algorithm is achieved in that
1. shear the positive face of the people's face approximate identical attitude people face composition training face database corresponding with equal number of a small amount of (20 get final product), these people's faces are carried out simple normalization.Normalization can keep the head full detail, removes image color information, determines behind the position of human eye human eye to be fixed on ad-hoc location, and image zooming is arrived identical size, at last image is carried out histogram equalization and handles;
2. the colourful attitude people's face of calculation training and the just average face of face, Figure of description 3 (a) is 45 ° of average side faces in the experiment, Figure of description 3 (b) is average just face.Average face is respectively the pixel summation on people's face correspondence position in the training storehouse to be averaged again, and the average face that the obtains detailed information that weakened has kept big profile information;
3. use average positive face to deduct average colourful attitude people's face and obtain positive face compensating operator under this attitude, Figure of description 3 (c) is the positive face compensating operator of 45 ° of side faces.This operator does not represent everyone identity, and its weakened identity information of people's face can remedy the positive face profile that people's face side face lacks;
4. people's face to be identified is carried out normalized, add that the positive face compensating operator under this attitude compensates, the people's face after the using compensation carries out the PCA recognition of face.Positive face compensating operator has compensated the positive face profile information that people's face to be identified lacks, and namely the corresponding eigenface information of the eigenwert that PCA is big has also reduced the part attitude deflection profile information that disturbs the PCA algorithm simultaneously.Colourful attitude facial image lacks positive face profile, and comprises unwanted part attitude deflection profile, and this also is to use the PCA algorithm to be difficult to carry out the reason of colourful attitude recognition of face.
Because the present invention has remedied the positive face profile information that lacks when various attitude people's faces carry out PCA identification, and positive face compensating operator obtains by average face, do not represent your identity identification information, thereby can not mislead the identification of PCA algorithm, finally improved the discrimination of PCA algorithm to colourful attitude people's face.
Compare with other technology, the present invention has following advantage:
1. use average face to calculate, do not adopt people's face in the face database is formed the method that large matrix is trained, reduced calculated amount significantly, improved computing velocity.
2. low to people's face normalization requirement, only people's face eyes position need be fixed on same position.A lot of algorithms are stable in order to guarantee algorithm, people's face cut to only contain eyes, nose and mouth, need the strict alignment of various piece, and the facial image pixel after shearing can not be excessive.Can keep all information of people's head among the present invention, the facial image pixel count is not required yet, the facial image pixel count is more high, and recognition effect is also more good.
3. face database is selected easily, and the training of human face number that needs seldom.The face database that needs has no special requirements, and uses current popular CMU PIE face database just can reach good effect, select arbitrarily wherein positive face and corresponding colourful attitude people's face respectively 20 train, can reach good recognition effect.
4. except basic PC A algorithm, the present invention only comprises simple plus and minus calculation, does not have to comprise matrix multiplication operation in a lot of colourful attitude face recognition algorithms, and calculated amount is obviously much smaller.
5. this algorithm can solve the recognition of face problem under the various attitudes, as have the identification of the side face of certain level deflection angle, have deflection angle up and down, different expressions, illumination, age, background, comprise the colourful attitude people's face under the various conditions such as beard, glasses, hair, the algorithm highly versatile.
Description of drawings
Accompanying drawing 1 shows PCA and decomposes the eigenface that obtains.(a) be maximum 10 eigenwert characteristic of correspondence faces (by the descending arrangement of eigenwert); (b) be minimum 10 eigenwert characteristic of correspondence faces (by the descending arrangement of eigenwert);
Accompanying drawing 2 shows entire process and the identifying of colourful attitude people's face;
Accompanying drawing 3 shows the average face and positive face compensating operator that obtains, and (a) is 45 ° of average side faces, (b) is average positive face, (c) is positive face compensating operator;
Accompanying drawing 4 shows colourful attitude people's face compensation and the recognition result without symmetrical treatment, (a) for the colourful attitude people's face after the compensation, (b) is the recognition result of compensator's face;
Accompanying drawing 5 shows colourful attitude people's face compensation and the recognition result of symmetrical treatment, (a) for the colourful attitude people's face after the compensation, (b) is the recognition result of compensator's face.
Embodiment
In order to understand the present invention better, will describe the specific embodiment of the present invention in detail below.
CMU PIE face database is to consider face database that colourful attitude people's face is identified in numerous face databases more fully, therefore uses CMU PIE face database to come the present invention is explained in detail.The X deflection angle that CMU PIE face database comprises 68 people for ± 22.5, ± 45 and ± 67.5 side face, people's face of certain luffing angle, totally 13 kinds of attitude conditions, the colourful attitude people's face under 43 kinds of illumination conditions and the 4 kinds of expressions.Select the people's face under the various attitude conditions to carry out normalization with corresponding positive face, then it is averaged people's face, obtain the average face of all angles, the average face under average positive face and each attitude is subtracted each other, obtain the positive face compensating operator under each attitude.Be described in detail for+45 side face is identified as example to horizontally rotate angle now, the recognition of face step under other colourful attitude conditions is duplicate.
At first be that people's face is carried out normalization, because that the present invention requires normalization is low, therefore only need remove the chromatic information of image, the human eye of+45 side faces and positive face image is fixed on assigned address, and with image zooming to identical size (as 100 * 100).At last, facial image is carried out histogram equalization, remove the noise effect that different light is brought.
Represent positive face with P0, represent that with Pk X deflection angle is+45 side face, supposed to use N people's positive face and side face to train, so after the normalization, N opens positive face and corresponding N and opens X deflection angle and constituted training sequence for+45 side face.Wherein, positive face set representations is
Side face set representations is
It is positive face
Corresponding side face image.
Then average positive face can be expressed as
Average side face can be expressed as
The present invention proposes the concept of positive face compensating operator, represents with Ω, and computing method are
If input side face image is
Then through after the positive face compensating operator compensation, facial image is expressed as
Side face to be identified is added a positive face compensating operator, and the side face after will compensating then uses the PCA face recognition algorithms to identify as identification people face.
Process to the identification of its colourful attitude people's face is the same, namely all be to train by this attitude people face and positive face, obtain the positive face compensating operator of this attitude condition at last, use this positive face compensating operator that people's face to be identified is compensated, be input to then in the PCA recognizer and identify.Part of test results is shown in Figure of description 4, (a) be with the effect of side face after by the compensation of positive face compensating operator, (b) result who identifies for the side face after the using compensation, it is identification error that this group people face directly uses the PCA algorithm to identify, identify but re-use the PCA algorithm after the compensation, can both obtain correct result.
In addition, the image of identification error is first carried out secondary identification, the error result that is about to identify is got rid of and is identified, and can find that the accuracy of identifying obviously improves.
Need to prove: by experience as can be known, concerning most people's faces, roughly be a symmetrical figure from positive angle, and for the side face that deflection is arranged, the quantity of information of the right and left is different.Because barrier effect, will be more than the quantity of information in the outside dorsad towards face's quantity of information in the outside, clear through the side face part laterally of positive face compensation.But replace people's face of Outboard Sections dorsad by the symmetrical treatment use towards people's face in the outside, the compensator's face after the use symmetry is identified, and discrimination does not improve, and has but reduced on the contrary, and part of test results is shown in Figure of description 5.Though inboard people's face lacks than the information that outside people's face comprises, inboard people's face comprises some face mask information and detailed information that Outboard Sections does not have.And the PCA algorithm is that whole people's face carried out holistic approach, rather than the coupling of individual element, and symmetrical treatment can be removed the information that Outboard Sections that inside part comprises does not have, thereby can cause discrimination to reduce.In addition, after the symmetrical treatment, the sudden change of image pixel appears in pars intermedia branch, is equivalent to introduce noise, also can identify people's face to PCA and exert an influence.
Claims (1)
1. the method for a colourful attitude recognition of face wherein based on the colourful attitude face recognition technology of PCA, compensates colourful attitude people's face by positive face compensating operator, and the people's face after the using compensation carries out colourful attitude recognition of face, it is characterized in that having adopted following steps:
A, people's face of shearing 20 positive face certain attitude corresponding with equal number are formed the training face database, and these people's faces are carried out simple normalized, only the eyes position need be fixed on ad-hoc location;
B, according to the sharpness of image with image zooming to identical size, and do gray processing and histogram equalization is handled, less than the requirement of a certain threshold value, image pixel number is more not high in principle for the facial image pixel count, recognition effect is also more good;
The average face of C, the colourful attitude people's face of calculating and positive face uses N people's positive face to train with people's face of corresponding certain attitude, and N opens positive face
Open P with corresponding N
kPeople's face of attitude
The composing training sequence, wherein
It is positive face
Corresponding P
kFacial image under the attitude, 1≤i≤N; Average positive face is expressed as
P
kAverage face under the attitude is expressed as
D, positive face outline compensation operator Ω of definition subtract P with average positive face
kAverage face under the attitude obtains
Positive face compensating operator under the attitude is expressed as
This operator does not represent everyone identity, and the identity information of the people's face that weakened has kept big profile information;
E, colourful attitude people's face to be entered is carried out identical normalized, and add that positive face compensating operator compensates, input people face
Through being expressed as after the positive face compensating operator compensation
Positive face compensating operator can be given prominence to the positive face profile information of people's face, eliminates the colourful attitude profile information that part is disturbed PCA identification, thus the big corresponding eigenface information of eigenwert in the compensation PCA algorithm;
F, for the first time during identification error, people's face that wrong identification is come out is got rid of, carried out secondary identification, can improve discrimination.
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| CN102779269B (en) * | 2012-06-13 | 2014-10-29 | 合肥工业大学 | Human face identification algorithm based on image sensor imaging system |
| CN103400105B (en) * | 2013-06-26 | 2017-05-24 | 东南大学 | Method identifying non-front-side facial expression based on attitude normalization |
| CN105590107B (en) * | 2016-02-04 | 2019-07-02 | 山东理工大学 | A kind of face low-level image feature construction method |
| CN106203393A (en) * | 2016-07-22 | 2016-12-07 | 广东金杭科技股份有限公司 | A kind of face collection and recognition method and the system realizing the method |
| CN106203400A (en) * | 2016-07-29 | 2016-12-07 | 广州国信达计算机网络通讯有限公司 | A kind of face identification method and device |
| CN106991448A (en) * | 2017-04-10 | 2017-07-28 | 广东金杭科技股份有限公司 | A kind of portrait compares processing method |
| CN107292299B (en) * | 2017-08-14 | 2018-10-30 | 河南工程学院 | Side face recognition methods based on kernel specification correlation analysis |
| CN107818305B (en) * | 2017-10-31 | 2020-09-22 | Oppo广东移动通信有限公司 | Image processing method, image processing device, electronic equipment and computer readable storage medium |
| CN107977610B (en) * | 2017-11-21 | 2021-10-12 | 电子科技大学 | Human face tracking method based on massive video processing |
| CN111199029B (en) * | 2018-11-16 | 2023-07-18 | 株式会社理光 | Face recognition device and face recognition method |
| CN109784243B (en) * | 2018-12-29 | 2021-07-09 | 网易（杭州）网络有限公司 | Identity determination method and device, neural network training method and device, and medium |
| JP7464619B2 (en) * | 2019-03-12 | 2024-04-09 | エレメント インク． | Detecting spoofing using facial recognition on mobile devices |
| CN110069655B (en) * | 2019-03-26 | 2022-07-12 | 中山大学 | Face searching method for private photo album |
| CN110826402B (en) * | 2019-09-27 | 2024-03-29 | 深圳市华付信息技术有限公司 | Face quality estimation method based on multitasking |
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