CN1595427A - Digital human face image recognition method based on selective multi-eigen space integration - Google Patents
Digital human face image recognition method based on selective multi-eigen space integration Download PDFInfo
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- CN1595427A CN1595427A CN200410041173.4A CN200410041173A CN1595427A CN 1595427 A CN1595427 A CN 1595427A CN 200410041173 A CN200410041173 A CN 200410041173A CN 1595427 A CN1595427 A CN 1595427A
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- 230000010354 integration Effects 0.000 title abstract 2
- 239000013598 vector Substances 0.000 claims abstract description 16
- 230000001815 facial effect Effects 0.000 claims description 32
- 239000004744 fabric Substances 0.000 claims description 9
- 238000002360 preparation method Methods 0.000 claims description 7
- 239000000284 extract Substances 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 2
- 238000000605 extraction Methods 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 claims 1
- 230000008921 facial expression Effects 0.000 abstract description 2
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Abstract
The invention relates to a digital human face image identification method based on the multiselective multiple eigenspaces integration. It is composed of the following steps. Acquire the training image set used to select the characteristic of the rectangular and the optimum rectangular characteristic set. Then form the target human face base. Generate an eigenspace for each selected rectangular characteristic and the projection vectors of the image to be identified in every eigenspace corresponding to the optimum rectangular characteristic set. Calculate the similarity degree between the image to be identified and the optimum rectangular characteristic set of each image in the target human face base. The object corresponding to the image with the maximum similarity degree in the target human face base is considered to be the result. The advantage of the invention is that it can still identify accurately even when there is a great facial expression variation of the object to be identified or the object is partly sheltered. In this way, the accuracy and the reliability of the identification equipment are improved.
Description
One, technical field
The present invention relates to digital facial image and detect and recognition methods, particularly a kind of recognition methods that is suitable for having the digital facial image of facial expression variation greatly or partial occlusion.
Two, background technology
The numeral facial image detects with recognition device and can be widely used in aspects such as identity document identification, buildings access and exit control, computer log control, credit cardholder's discriminating, criminal's tracking, accident detection.Carry out the method for identity discriminating with other human body biological characteristics such as utilizing fingerprint, iris and compare, end user's face is differentiated friendly more and convenient.
Yet there is the problem of two aspects at least in existing digital facial image recognition technology: the first, and in actual applications, the expression of object to be identified usually has bigger variation, and hair, glasses, beard etc. usually can cause partial occlusion.In these cases, prior art is difficult to obtain the accurate recognition result, and digital facial image detects and the widespread use of recognition device thereby also just limited.The second, the position in the zone in the employed facial image of prior art, size, number etc. are mainly determined according to subjective experience.For example, classical intrinsic face method directly utilizes the view picture facial image to discern, and facial characteristics regions such as intrinsic characteristics method utilization eye, nose, mouth are discerned.Yet, can't prove objectively that using these image-regions just to discern can obtain optimum efficiency, can't know that also how controlling each regional scope just can make full use of the information that comprises in the facial image.
Three, summary of the invention
The objective of the invention is problem, utilize the selectivity in the machine learning integrated, provide a kind of and can select several rectangular areas in the facial image to combine the method for carrying out recognition of face automatically at existing digital facial image recognition technology existence.
For achieving the above object, the invention provides a kind of based on the integrated digital facial image recognition method of the many eigen spaces of selectivity, before this method is specifically described, at first provide relevant definition: (a) rectangular characteristic: any rectangular area in the digital facial image.(b) similarity of rectangular characteristic: the similarity between the projection vector of two rectangular characteristic in corresponding eigen space.(c) best rectangular characteristic set: the set that several rectangular characteristic that choose with rectangular characteristic system of selection provided by the invention constitute, the rectangular characteristic during this is gathered combine to be carried out recognition of face and can obtain effect preferably.(d) similarity of best rectangular characteristic set: the similarity sum of two width of cloth images corresponding rectangular characteristic in best rectangular characteristic set.(e) target face database: the image library of personage's photo of storage known identities, image to be identified by with this image library to recently determining one's identity.
Face identification method provided by the invention comprises following key step: (1) if recognition mechanism does not train, then execution in step 2, otherwise forward step 7 to; (2) acquisition is used to select the training plan image set of rectangular characteristic; (3) select best rectangular characteristic set according to the training plan image set; (4) obtain the target face database; (5) be training data with the image in the target face database, for each rectangular characteristic that chooses generates an eigen space; (6) projection vector of every width of cloth image in the target face database in pairing each eigen space of best rectangular characteristic set preserved; (7) receive image to be identified; (8) generate the projection vector of image to be identified in pairing each eigen space of best rectangular characteristic set; (9) calculate the similarity that the best rectangular characteristic of each width of cloth image is gathered in image to be identified and the target face database; (10) with the object of the target face database image correspondence of best rectangular characteristic set similarity maximum as recognition result; (11) finish.
The present invention compared with prior art, its remarkable advantage is: still can discern more exactly even this method has big expression shape change or exists under the situation of partial occlusion at object to be identified, detect accuracy and reliability with recognition device thereby help to improve digital facial image.
Four, description of drawings
Fig. 1 is that digital facial image detects and the recognition device workflow diagram.
Fig. 2 is the process flow diagram of the inventive method.
Fig. 3 is a process flow diagram of selecting best rectangular characteristic set.
Five, embodiment
The present invention is described in detail below in conjunction with accompanying drawing and most preferred embodiment.
As shown in Figure 1, digital facial image detects with recognition device and obtains gray level image by Digital Image Input Device, utilizes the information of eye location to obtain human face region then.Just handle then, generally include facial image is carried out feature extraction and compares with the existing image in the facial image database by recognition mechanism.
Method of the present invention as shown in Figure 2.Step 10 is initial actuatings.Step 11 judges whether recognition mechanism trains, if execution in step 19 then; Otherwise execution in step 12.Step 12 judges whether the set of best rectangular characteristic chooses, if execution in step 15 then; Otherwise execution in step 13.Step 13 is obtained the training plan image set that is used to select best rectangular characteristic set, and everyone will have two facial images at least in this image set, and wherein a conduct prepares image, and all the other are as authentication image.Step 14 is selected the set of best rectangular characteristic according to the training plan image set, comprises m rectangular characteristic in this set, and m is the round values of user's appointment here, and for example 4.This selection course adopts the custom-designed rectangular characteristic selection algorithm of the present invention, and this step will be specifically introduced in conjunction with Fig. 3 in the part of back.
After the rectangular characteristic set chose, step 15 was obtained the target face database, and each object has a width of cloth facial image at least in this storehouse.Step 16 extracts the best rectangular characteristic set of all images in the target face database.Step 17 is a raw data with all target face database images, utilize the principal component analysis (PCA) technology in the higher algebra textbook, be that each rectangular characteristic in the best rectangular characteristic set generates the eigen space that K orthogonal basis vector arranged, K is the round values of user preset here, for example 20.Step 18 is utilized the vector operations in the higher algebra textbook, the rectangular characteristic that chooses of each width of cloth image in the database is projected in its corresponding eigen space, and the projection vector that obtains is recorded in the target face database, like this, each width of cloth image in the target face database just corresponding some projection vectors.
The step 19 of Fig. 2 receives facial image to be identified, and 16 way generates the best rectangular characteristic set of this image set by step then.Step 21 projects to each rectangular characteristic in the best rectangular characteristic set in the corresponding eigen space, obtains one group of projection vector.Step 22 projection vector that each projection vector of image to be identified is corresponding with each image in the database is compared.Two vectors are compared can utilize technology in the probability statistics textbook, finish by asking the related coefficient between them, the big more then similarity of related coefficient is big more.Step 23 is found out in the target face database and the maximum image of the similarity of the best rectangular characteristic set of image to be identified (the similarity sum of all projection vector of two width of cloth images just), and with the object (being the owner of people's face) under this image as recognition result.At last, step 24 is done states.
It is worthy of note that the step 13 to 14 of Fig. 2 is specifically designed to selects best rectangular characteristic set, in a single day this set is selected, just can be directly used in different target face databases.Therefore in a single day, the inventive method only need be carried out one time selection course when being applied to different target face databases, has promptly determined best rectangular characteristic set, just can leap to step 15 from step 12.In addition, if the scale of target face database is enough big, training corresponding to many eigen spaces of best rectangular characteristic set also can once be finished, and promptly step 17 also can only be carried out once, just the many eigen spaces that train can be directly applied to different face databases afterwards.
Fig. 3 describes the step 14 of Fig. 2 in detail, and the effect of this step is the set with m rectangular characteristic of selecting recognition effect the best according to the training plan image set, and m is the round values of user's appointment here.The step 1400 of Fig. 3 is initial states.Step 1401 is obtained all preparation images.Step 1402 extracts all rectangular characteristic of all preparation images.Step 1403 is a raw data with all preparation images, utilizes the principal component analysis (PCA) technology to generate an eigen space for each rectangular characteristic.Step 1404 is discerned all authentication image in each eigen space, be about to authentication image and the projection in eigen space respectively of the preparation corresponding rectangular characteristic of image, find out similarity is the highest between projection vector preparation image as recognition result, and the recognition correct rate of all authentication image is noted as the accuracy of respective rectangular feature.
The step 1405 of Fig. 3 is created a candidate rectangle characteristic set A, wherein comprises preceding n the highest rectangular characteristic of accuracy, and the n here is the round values of user's appointment, and for example 1000.Step 1406 is created a S set, wherein initially puts into the highest rectangular characteristic of A accuracy, simultaneously this rectangular characteristic is deleted from A.Step 1407 judges whether comprised m rectangular characteristic among the S, if words execution in step 1410, otherwise execution in step 1408.
Each rectangular characteristic among the step 1408 of Fig. 3 couple A, calculating it can discern correct and have at least the number c of the authentication image of a rectangular characteristic identification error, the value of c to regard as among the S to have reflected the correction capability that rectangular characteristic is known rectangular characteristic mistake among the S among the A.Next, step 1409 rectangular characteristic that the c value is maximum is deleted from A and is joined among the S, and then gets back to the number that step 1407 is judged rectangular characteristic among the S.If comprised m rectangular characteristic among the S this moment, then in step 1410, S is gathered as best rectangular characteristic.At last, step 1411 is done states of Fig. 3.
The inventive method is not only to use single facial image full figure, carry out recognition of face but a plurality of rectangular areas in the facial image are combined, like this, this method had both been utilized the global information of facial image, utilized the local message of facial image again, still can accurately discern even therefore have under big expression shape change or the facial situation of blocking of part at object to be identified.In the selection course of best rectangular characteristic set, based on the integrated thought of the selectivity in the machine learning, 2 points have mainly been considered, the single Feature Recognition accuracy of the first, it two is that certain feature is corrected the ability that other features mistake is known, so make the rectangular characteristic set that finally chooses not only have higher resolving power but also have stronger complementarity, thereby the rectangular characteristic in having guaranteed to gather combine and can obtain recognition effect preferably.Therefore, rectangular characteristic system of selection shown in Figure 3 is a core of the present invention.
Claims (2)
1, a kind of based on the integrated digital facial image recognition method of the many eigen spaces of selectivity, comprise by Digital Image Input Device with gray level image be input to digital facial image detection and recognition device, to facial image carry out feature extraction and with facial image database in the identification of comparing of existing image, it is characterized in that described face identification method comprises following key step:
(1), then carries out (2), otherwise forward (7) to if recognition mechanism does not train;
(2) acquisition is used to select the training plan image set of rectangular characteristic;
(3) select best rectangular characteristic set according to the training plan image set;
(4) obtain the target face database;
(5) be training data with the image in the target face database, for each rectangular characteristic that chooses generates an eigen space;
(6) projection vector of every width of cloth image in the target face database in pairing each eigen space of best rectangular characteristic set preserved;
(7) receive image to be identified;
(8) generate the projection vector of image to be identified in pairing each eigen space of best rectangular characteristic set;
(9) calculate the similarity that the best rectangular characteristic of each width of cloth image is gathered in image to be identified and the target face database;
(10) with the object of the target face database image correspondence of best rectangular characteristic set similarity maximum as recognition result;
(11) finish.
2, according to claim 1 based on the integrated digital facial image recognition method of the many eigen spaces of selectivity, next is characterised in that the described step of selecting best rectangular characteristic to gather according to the training plan image set is:
(1) obtains all preparation images;
(2) extract all rectangular characteristic that all prepare images;
(3) be raw data with all preparation images, utilize principal component analytical method to generate an eigen space for each rectangular characteristic;
(4) in each eigen space, all authentication image are discerned;
(5) create a candidate rectangle characteristic set A, wherein comprise preceding n the highest rectangular characteristic of accuracy, n is the round values of an appointment;
(6) create a S set, wherein initially put into the highest rectangular characteristic of A accuracy, simultaneously this rectangular characteristic is deleted from A;
(7) judge whether comprised m rectangular characteristic among the S, if then carry out (10), otherwise carry out (8), m is the round values of appointment;
(8) step is calculated it and can be discerned the number c that correctly has the authentication image of a rectangular characteristic identification error among the S at least each rectangular characteristic among the A;
(9) the c value is a maximum rectangular characteristic delete from A and is joined among the S, and then gets back to the number of rectangular characteristic among step (7) the judgement S, if comprised m rectangular characteristic among the S, and execution in step (10) then;
(10) S is gathered as best rectangular characteristic;
(11) finish.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101354728B (en) * | 2008-09-26 | 2010-06-09 | 中国传媒大学 | Method for measuring similarity based on interval right weight |
| CN101369310B (en) * | 2008-09-27 | 2011-01-12 | 北京航空航天大学 | Robust human face expression recognition method |
| CN101281540B (en) * | 2007-04-04 | 2011-05-18 | 索尼株式会社 | Apparatus, method and computer program for processing information |
| CN103927554A (en) * | 2014-05-07 | 2014-07-16 | 中国标准化研究院 | Image sparse representation facial expression feature extraction system and method based on topological structure |
| CN108491824A (en) * | 2018-04-03 | 2018-09-04 | 百度在线网络技术（北京）有限公司 | model generating method and device |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4483042B2 (en) * | 2000-07-12 | 2010-06-16 | コニカミノルタホールディングス株式会社 | Shadow component removal apparatus and shadow component removal method |
| CN1209731C (en) * | 2003-07-01 | 2005-07-06 | 南京大学 | Automatic human face identification method based on personal image |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101281540B (en) * | 2007-04-04 | 2011-05-18 | 索尼株式会社 | Apparatus, method and computer program for processing information |
| CN101354728B (en) * | 2008-09-26 | 2010-06-09 | 中国传媒大学 | Method for measuring similarity based on interval right weight |
| CN101369310B (en) * | 2008-09-27 | 2011-01-12 | 北京航空航天大学 | Robust human face expression recognition method |
| CN103927554A (en) * | 2014-05-07 | 2014-07-16 | 中国标准化研究院 | Image sparse representation facial expression feature extraction system and method based on topological structure |
| CN108491824A (en) * | 2018-04-03 | 2018-09-04 | 百度在线网络技术（北京）有限公司 | model generating method and device |
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