CN103530648A - Face recognition method based on multi-frame images - Google Patents
Face recognition method based on multi-frame images Download PDFInfo
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- CN103530648A CN103530648A CN201310477125.9A CN201310477125A CN103530648A CN 103530648 A CN103530648 A CN 103530648A CN 201310477125 A CN201310477125 A CN 201310477125A CN 103530648 A CN103530648 A CN 103530648A
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Abstract
The invention relates to the field of security and protection, in particular to a face recognition method based on multi-frame images. The method concretely comprises the following steps of 1, personnel identity registration; 2, data collection of personnel to be recognized: multi-frame images containing faces of personnel to be recognized are collected to be used as original sample sequences to be recognized, wherein J is the face image collection times of the personnel to be recognized; 3, face recognition: after personnel to be recognized enter a recognition region for data collection, the original registration sample sequence is inquired according to the identity information of personnel to be recognized, and the registration sample sequence corresponding to the personnel to be recognized is obtained; the orthogonal subspace of the registration sample sequence and the orthogonal subspace of the sample sequence to be recognized are calculated, the similarity measure of the two subspaces is calculated, and whether the faces to be recognized are consistent with registration faces or not is judged according to the similarity measure of the two subspaces. The similarity measure of the two subspaces is compared, and the faces are recognized on the basis of multi-frame images.
Description
Technical field
The present invention relates to technical field of security and protection, relate in particular to a kind of face identification method based on multiple image, be applicable to the higher occasion of accuracy of identification and stability requirement.
Background technology
What the personnel identity identification equipment of available technology adopting adopted conventionally is access card system, the access card that different personnel assignments is different, after access card confirmation is errorless, gate is opened and is allowed to pass through, it is not high that such mode is applicable to safety requirements, personnel and access card do not need occasion one to one, such as the mess card in mass transit card, dining room etc.But such mode can not be carried out corresponding one by one with personnel to be identified by access card, also just there is certain potential safety hazard, especially in the higher occasion of demand for security, above-mentioned mode obviously cannot solve the problem of borrowing card or stealing card, such as identification on airport or when the safe procuratorial work in railway station, bank debits number are larger etc., these have higher demand for security, the access card of only usining is obviously inadequate as the mode of identification, also needs the information in personnel to be identified and access card to mate.This also just needs a kind of method that people's face of registration in people's face of personnel to be identified and card is judged.
Recognition of face of the prior art mainly adopts following two kinds.The first, gather frame people face data, people's face of preserving in this frame people's face collecting and database is compared, judge the similarity between two two field pictures, thereby identify whether people's face of preserving in people's face of collection and database is same person.Yet while differing greatly when people's face angle to be identified, attitude, light or expression and registration, discrimination significantly reduces, and causes and repeatedly tests discrimination fluctuation greatly.For this problem, in prior art, occurred that another many camera lens gathers the method for people's face to be identified, camera lens is installed respectively in the positions such as front, rear, left and right personnel to be identified, adopt a plurality of camera lenses to obtain multiframe people's face of synchronization different angles, people's face that each camera lens is obtained respectively one by one with database in registrant's face of preserving compare one by one.Although it is a plurality of that sample point to be identified has, but still be comparing between sample point and sample point, lack the related information between sample point, the distributed intelligence of the attitude that is beyond expression, light, expression.And because the introducing of a plurality of camera lenses, hardware cost is higher, system stability reduces.
Summary of the invention
The discrimination occurring when personnel's to be identified attitude, light, the expression shape change for recognition of face of the prior art reduces, the technical matters of identification poor stability, the invention discloses a kind of face identification method based on multiple image.
The invention discloses a kind of face identification method based on multiple image, it specifically comprises the following steps:
Step 1, personnel identity registration: gather the image that multiframe comprises accredited personnel people's face, the image that comprises accredited personnel people's face collecting is carried out to the detection of people's face, extract the people's face in each image and preserve, be called accredited personnel's the bashful figure of people; By the bashful figure of a plurality of accredited personnel's people carry out yardstick, towards the normalized with illumination, the bashful figure of people after normalized is as original licensed sample sequence
, wherein K is
the number of times of individual accredited personnel's man face image acquiring, the total quantity that N is accredited personnel;
Step 2, personnel's data acquisition to be identified: gather the image that multiframe comprises personnel people's face to be identified, the image that comprises personnel people's face to be identified collecting is carried out to the detection of people's face, extract the people's face in each image and preserve, the bashful figure of people that is called personnel to be identified, and by the bashful figure of a plurality of people carry out yardstick, towards the normalized with illumination, the bashful figure of people after normalized is as original sample sequence to be identified
, wherein J is the number of times of personnel's man face image acquiring to be identified;
Step 3, recognition of face: personnel to be identified enter identified region and carry out after data acquisition, according to personnel identity information inquiry original licensed sample sequence to be identified, obtain registration sample sequence corresponding to personnel to be identified
; Calculate registration sample sequence
orthogonal subspaces
with sample sequence to be identified
orthogonal subspaces
, calculate two sub spaces
with
similarity measure, according to the similarity measure of two sub spaces, judge that whether people's face to be identified consistent with registrant's face.
Further, said method also comprises: when carrying out authentication, while confirming whether people to be identified is he or she, registrant's face of people's face to be identified and appointment carries out subspace and compares, subspace similarity measure compares with the threshold value of setting in advance, thereby judges.
Further, said method also comprises: when carrying out identification, while confirming that whom people to be identified is, the registrant's face in people's face to be identified and database carries out subspace one by one to be compared, or finds out the registrant face classification the most similar to it by sorter.
Further, the method for the similarity measure between above-mentioned judgement two sub spaces is the main included angle cosine between judgement two sub spaces.
Further, the detailed process of the main included angle cosine between above-mentioned judgement two sub spaces is: the angle of setting between two sub spaces is
, the angle between two sub spaces
be less than the angle threshold value of setting
time, judge that personnel to be identified are consistent with accredited personnel, identify successfully; Otherwise judge that personnel to be identified and accredited personnel are inconsistent, recognition failures; Wherein
.
Further, above-mentioned people's face testing process is specific as follows:
First adopt Haar characteristic image to travel through in the image of 20 * 20, the pixel of white portion and deduct black region pixel and, the value obtaining is referred to as face characteristic value;
Then from the face database of registration in advance, cut out a large amount of people's face picture and background picture, as training sample; Training sample is normalized to the image of 20 * 20 sizes, in big or small like this picture, extracts Haar eigenwert; These Haar features have formed Weak Classifier, and its function expression is:
Wherein:
the image of 20 * 20 sizes,
for Haar feature,
for symbol designator,
for threshold value.
Further, said method also comprises training strong classifier, and the training process of strong classifier need to pass through
inferior iteration, its detailed process is as follows:
(1) given training sample set
, altogether
individual sample, wherein
with
correspond respectively to positive sample and negative sample;
maximum cycle for training;
(2) initialization sample weight is
, be the initial probability distribution of training sample;
(3) iteration training for the first time
individual sample, obtains first optimum Weak Classifier;
(4) improve last round of in the weight of misjudged sample;
(5) new sample and last time are put together and carried out the training of a new round by the sample of misclassification;
(6) 4-5 step is carried out in circulation,
after wheel, obtain
individual optimum Weak Classifier;
(7) combination
individual optimum Weak Classifier obtains strong classifier, and array mode is as follows
Further, said method also comprises face characteristic leaching process, and the registrant's face collecting in step 1 and step 2 and people's face to be identified are carried out carrying out the recognition of face of step 3 after feature extraction again.
Further, above-mentioned feature extracting method is principal component analysis (PCA).
By adopting above technical scheme, beneficial effect of the present invention is: in identification and two stages of identity registration, all gather respectively multiple image, and carry out respectively the detection of people's face, the multiframe facial image recognizing is set up respectively to subspace separately, calculate the similarity measure between two sub spaces, thereby judge that whether people's face to be identified is similar to registrant's face.The present invention adopts the method for comparing between two sub spaces, and identification accuracy and stability are all improved, and compares with the scheme of a plurality of camera lenses simultaneously, and hardware cost is lower, and system stability improves.The present invention adopts same video camera to take multiframe facial image at cognitive phase, this multiframe facial image is formed to subspace, and compare with the subspace that the multiframe facial image in when registration forms, in the situation that not increasing hardware device, greatly improved the accuracy of identification.
Accompanying drawing explanation
Fig. 1 is the face identification method process flow diagram based on multiple image of the present invention.
Embodiment
In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with Figure of description and specific embodiment, the present invention is described in further detail.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.
The face identification method process flow diagram based on multiple image of the present invention as shown in Figure 1.Its concrete steps comprise identity registration and two stages of identification; In the identity registration stage, first sequentially everyone is carried out to multiple image collection, every two field picture recognition of face, face characteristic extraction and face characteristic record; In the identification stage, equally sequentially carry out multiple image collection, every two field picture recognition of face, face characteristic extraction and face characteristic record; Then according to personnel identity information to be identified, (such as No. ID, identity) obtains the corresponding face characteristic at registration phase record, the face characteristic that the identification stage obtains is measured with the similarity that the corresponding face characteristic of registration phase record carries out two sub spaces, whether legal thereby personnel to be identified are judged in knowledge.Such as, when personnel to be identified are legal, gate is opened, and permission personnel pass through, otherwise gate is not opened, and sends alerting signal simultaneously.
Embodiment particularly:
The invention discloses a kind of face identification method based on multiple image, it specifically comprises the following steps:
Step 1, personnel identity registration: gather the image that multiframe comprises accredited personnel people's face, such as requiring accredited personnel face in the face of camera lens, conversion towards (face, upward, down, towards left, towards the right side) carry out multi collect, the image that comprises accredited personnel people's face collecting is carried out to the detection of people's face, extract the people's face in each image and preserve, the bashful figure of people that is called accredited personnel, and by the bashful figure of a plurality of accredited personnel's people carry out yardstick, towards the normalized with illumination.Yardstick normalization: refer to the facial image of different sizes is zoomed in or out to unified size; Towards normalization: refer to the facial image tilting is rotated to normal angled; Unitary of illumination: refer to the brightness that increases or reduce facial image, make facial image overall brightness consistent.Yardstick, towards the detailed process with unitary of illumination, belong to the conventional means of those skilled in the art, do not repeat them here its detailed process.The bashful figure of people after normalization is as an original licensed sample sequence
, wherein K is
the number of times of individual accredited personnel's man face image acquiring,
also can be set as accredited personnel's No. ID, identity.Personnel to be identified by time, according to No. ID, its identity
can obtain fast the image that personnel to be identified gather when registration.
Step 2, personnel's data acquisition to be identified: gather the image that multiframe comprises personnel people's face to be identified, the image that comprises personnel people's face to be identified collecting is carried out to the detection of people's face, extract the people's face in each image and preserve, the bashful figure of people that is called personnel to be identified, and by the bashful figure of a plurality of people carry out yardstick, towards the normalized with illumination.Yardstick normalization: refer to the facial image of different sizes is zoomed in or out to unified size; Towards normalization: refer to the facial image tilting is rotated to normal angled; Unitary of illumination: refer to the brightness that increases or reduce facial image, make facial image overall brightness consistent.Similarly, yardstick, towards the normalized detailed process with illumination, belong to that those skilled in the art commonly use technological means, do not repeat them here its detailed process.The bashful figure of people after normalization is as an original sample sequence to be identified
, wherein J is the number of times of personnel's man face image acquiring to be identified.
Step 3, recognition of face: personnel to be identified enter identified region and carry out after data acquisition, according to No. ID, identity
query sample database, obtains registration sample sequence corresponding to personnel to be identified
; Calculate registration sample sequence
orthogonal subspaces
with sample sequence to be identified
orthogonal subspaces
, calculate two sub spaces
with
similarity measure, according to the similarity measure of two sub spaces, judge that whether people's face to be identified consistent with registrant's face.When carrying out authentication, while confirming whether people to be identified is he or she, registrant's face of people's face to be identified and appointment carries out subspace to be compared, and subspace similarity measure compares with the threshold value of setting in advance, thereby judges.When carrying out identification, while confirming that whom people to be identified is, the registrant's face in people's face to be identified and database carries out subspace one by one to be compared, or finds out the registrant face classification the most similar to it by sorter.Judge that the similarity measure between two sub spaces can adopt the main included angle cosine between judgement two sub spaces to realize, also can select the similarity measure between other subspaces to calculate, such as: Kernel Principal Angles, Symmetric Distance algorithm.The detailed process that judges the main included angle cosine between two sub spaces is: the angle of setting between two sub spaces is
, the angle between two sub spaces
be less than the angle threshold value of setting
time, judge that personnel to be identified are consistent with accredited personnel, identify successfully; Otherwise judge that personnel to be identified and accredited personnel are inconsistent, recognition failures.Wherein
Prior art adopts many camera lenses to gather facial image to be identified, obtain the multiframe facial image of synchronization different angles, although it is a plurality of that sample point to be identified has, but just compare with registration sample point one by one, remain comparison between points, lack related information between sample point, the distributed intelligence of the attitude that is beyond expression, light, expression.Compare with the comparison method between sample of the present invention subspace, identification accuracy and less stable, and because the introducing of a plurality of camera lenses, hardware cost is higher, and system stability reduces.The present invention gathers respectively multiple image in identification and two stages of identity registration, and carry out respectively the detection of people's face, the multiframe facial image recognizing is set up respectively to subspace separately, calculate the similarity measure between two sub spaces, thereby judge that whether people's face to be identified is similar to registrant's face.The present invention adopts the method for comparing between two sub spaces, and identification accuracy and stability are all improved, and compares with the scheme of a plurality of camera lenses simultaneously, and hardware cost is lower, and system stability improves.The present invention adopts same video camera to take multiframe facial image at cognitive phase, this multiframe facial image is formed to subspace, and compare with the subspace that the multiframe facial image in when registration forms, in the situation that not increasing hardware device, greatly improved the accuracy of identification.
The process that said extracted goes out the people's face in each image can adopt people's face detection mode conventional in prior art, such as adopting HOG algorithm, SIFT algorithm etc.; Sorter can be used SVM, neural network etc.
People's face testing process in the present invention is specific as follows:
First adopt Haar characteristic image to travel through in the image of 20 * 20, the pixel of white portion and deduct black region pixel and, the value obtaining is referred to as face characteristic value.
Then from the face database of registration in advance, cut out a large amount of people's face picture and background picture, as training sample.Training sample is normalized to the image of 20 * 20 sizes, in big or small like this picture, extracts Haar eigenwert.These Haar features have formed Weak Classifier, and its function expression is:
Wherein:
the image of 20 * 20 sizes,
for Haar feature,
for symbol designator,
for threshold value.
The training process of strong classifier need to pass through
inferior iteration:
(1) given training sample set
, altogether
individual sample, wherein
with
correspond respectively to positive sample and negative sample;
maximum cycle for training;
(2) initialization sample weight is
, be the initial probability distribution of training sample;
(3) iteration training for the first time
individual sample, obtains first optimum Weak Classifier;
(4) improve last round of in the weight of misjudged sample;
(5) new sample and last time are put together and carried out the training of a new round by the sample of misclassification;
(6) 4-5 step is carried out in circulation,
after wheel, obtain
individual optimum Weak Classifier;
(7) combination
individual optimum Weak Classifier obtains strong classifier, and array mode is as follows
Because the training stage has been done yardstick normalization to training sample, so this strong classifier can only be checked through the people's face under same yardstick.In order further to improve the stability of algorithm, can 5 yardsticks (such as: original image is first yardstick, 4 yardsticks are below by obtaining original image size reduction to 1/2,1/4,1/8,1/16 of original size) 5 strong classifiers of lower training, between them, by cascade, form final sorter.
Further, said method also comprises face characteristic leaching process, and the registrant's face collecting in step 1 and step 2 and people's face to be identified are carried out carrying out the recognition of face of step 3 after feature extraction again.Generally, the subwindow that comprises facial image detecting can not be directly used in recognition of face, and this is that excessive dimension can cause " dimension disaster " problem because these subwindows generally comprise the pixel of 400 left and right.Secondly, because the human face expression in these subwindows differs, even also have noise, block the situation mixed and disorderly with background.Therefore processing mode is by characteristic extraction procedure preferably, reduces the dimension of data, reduces picture noise, promotes the conspicuousness of data.In numerous feature extraction algorithms, principal component analysis (PCA) (Principal Component Analysis) is because it has analytic solution, and without iteration, counting yield is high and be used widely.Therefore, the preferred mode of the present invention is to adopt principal component analytical method to extract face characteristic.
Given coefficient and parameter in the above embodiments; be to provide to those skilled in the art and realize or use of the present invention; the present invention does not limit and only gets aforementioned disclosed numerical value; in the situation that not departing from inventive concept; those skilled in the art can make various modifications or adjustment to above-described embodiment; thereby protection scope of the present invention do not limit by above-described embodiment, and it should be the maximum magnitude that meets the inventive features that claims mention.
Claims (9)
1. the face identification method based on multiple image, it specifically comprises the following steps:
Step 1, personnel identity registration: gather the image that multiframe comprises accredited personnel people's face, the image that comprises accredited personnel people's face collecting is carried out to the detection of people's face, extract the people's face in each image and preserve, be called accredited personnel's the bashful figure of people; By the bashful figure of a plurality of accredited personnel's people carry out yardstick, towards the normalized with illumination, the bashful figure of people after normalized is as original licensed sample sequence
, wherein K is
the number of times of individual accredited personnel's man face image acquiring, the total quantity that N is accredited personnel;
Step 2, personnel's data acquisition to be identified: gather the image that multiframe comprises personnel people's face to be identified, the image that comprises personnel people's face to be identified collecting is carried out to the detection of people's face, extract the people's face in each image and preserve, the bashful figure of people that is called personnel to be identified, and by the bashful figure of a plurality of people carry out yardstick, towards the normalized with illumination, the bashful figure of people after normalized is as original sample sequence to be identified
, wherein J is the number of times of personnel's man face image acquiring to be identified;
Step 3, recognition of face: personnel to be identified enter identified region and carry out after data acquisition, according to personnel identity information inquiry original licensed sample sequence to be identified, obtain registration sample sequence corresponding to personnel to be identified
; Calculate registration sample sequence
orthogonal subspaces
with sample sequence to be identified
orthogonal subspaces
, calculate two sub spaces
with
similarity measure, according to the similarity measure of two sub spaces, judge that whether people's face to be identified consistent with registrant's face.
2. the face identification method based on multiple image as claimed in claim 1, it is characterized in that described method also comprises: when carrying out authentication, while confirming whether people to be identified is he or she, registrant's face of people's face to be identified and appointment carries out subspace and compares, subspace similarity measure compares with the threshold value of setting in advance, thereby judges.
3. the face identification method based on multiple image as claimed in claim 1, it is characterized in that described method also comprises: when carrying out identification, while confirming that whom people to be identified is, registrant's face in people's face to be identified and database carries out subspace one by one to be compared, or finds out the registrant face classification the most similar to it by sorter.
4. the face identification method based on multiple image as claimed in claim 1, is characterized in that the method for the similarity measure between described judgement two sub spaces is for judging the main included angle cosine between two sub spaces.
5. the face identification method based on multiple image as claimed in claim 4, is characterized in that the detailed process of the main included angle cosine between described judgement two sub spaces is: the angle of setting between two sub spaces is
, the angle between two sub spaces
be less than the angle threshold value of setting
time, judge that personnel to be identified are consistent with accredited personnel, identify successfully; Otherwise judge that personnel to be identified and accredited personnel are inconsistent, recognition failures; Wherein
.
6. the face identification method based on multiple image as claimed in claim 1, is characterized in that described people's face testing process is specific as follows:
First adopt Haar characteristic image to travel through in the image of 20 * 20, the pixel of white portion and deduct black region pixel and, the value obtaining is referred to as face characteristic value;
Then from the face database of registration in advance, cut out a large amount of people's face picture and background picture, as training sample; Training sample is normalized to the image of 20 * 20 sizes, in big or small like this picture, extracts Haar eigenwert; These Haar features have formed Weak Classifier, and its function expression is:
Wherein:
the image of 20 * 20 sizes,
for Haar feature,
for symbol designator,
for threshold value.
7. the face identification method based on multiple image as claimed in claim 6, is characterized in that described method also comprises training strong classifier, and the training process of strong classifier need to pass through
inferior iteration, its detailed process is as follows:
(1) given training sample set
, altogether
individual sample, wherein
with
correspond respectively to positive sample and negative sample;
maximum cycle for training;
(2) initialization sample weight is
, be the initial probability distribution of training sample;
(3) iteration training for the first time
individual sample, obtains first optimum Weak Classifier;
(4) improve last round of in the weight of misjudged sample;
(5) new sample and last time are put together and carried out the training of a new round by the sample of misclassification;
(6) 4-5 step is carried out in circulation,
after wheel, obtain
individual optimum Weak Classifier;
(7) combination
individual optimum Weak Classifier obtains strong classifier, and array mode is as follows
8. the face identification method based on multiple image as claimed in claim 1, it is characterized in that described method also comprises face characteristic leaching process, the registrant's face collecting in step 1 and step 2 and people's face to be identified are carried out carrying out the recognition of face of step 3 after feature extraction again.
9. the face identification method based on multiple image as claimed in claim 8, is characterized in that described feature extracting method is principal component analysis (PCA).
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