CN103207995A - PCA (Principal Component Analysis)-based 3D (three dimensional) face identification method - Google Patents
PCA (Principal Component Analysis)-based 3D (three dimensional) face identification method Download PDFInfo
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Abstract
The invention relates to a PCA (Principal Component Analysis)-based 3D (three dimensional) face identification method. The method comprises the steps of: firstly creating a database including facial image samples; carrying out processing and training on the facial image samples in the database, processing facial images collected by a camera or a scanner; and finally comparing and identifying processed to-be-identified facial images with the processed and trained facial image samples, and outputting an identification result. According to the PCA-based 3D face identification method provided by the invention, an improved PCA algorithm is adopted to identify the images, and the identification efficiency is improved; and a 3D facial deformation model method is adopted to establish a 3D facial model method, and the facial identification precision is improved.
Description
Technical field
The present invention relates to a kind of face identification method, relate in particular to a kind of 3D face identification method based on PCA.
Background technology
Recognition of face is refered in particular to utilize to analyze and is compared the computer technology that people's face visual signature information is carried out the identity discriminating.Recognition of face is the computer technology research field of a hot topic, and the face tracking detecting is adjusted image automatically and amplified, and exposure intensity is adjusted in infrared detecting at night automatically; It belongs to biometrics identification technology, is the biological characteristic of biosome (generally refering in particular to the people) itself is distinguished the biosome individuality.In face recognition process, generally include the detection of people's face, face characteristic extraction and recognition of face step, feature extraction is an important step that influences recognition effect.The 2D face recognition technology is ripe at present, but because single 2D image can not provide identification required complete information, and the face characteristic of its feature extraction also is based on the face characteristic of 2D, therefore, accuracy of identification is difficult to further improve.
Summary of the invention
The present invention has overcome the deficiencies in the prior art, and a kind of 3D of extraction face characteristic, face identification method that accuracy of identification is high are provided.
For achieving the above object, the technical solution used in the present invention is: a kind of 3D face identification method based on PCA is characterized in that: comprise following steps:
1) creates the database with facial image sample;
2) the facial image sample in the database being carried out detection and location, whether in image have people face, if people's face is arranged, then make accurate in locating if judging;
3) the facial image sample after the detection and location is carried out pre-service;
4) pretreated facial image sample is carried out feature extraction;
5) utilize the BP neural network to the facial image sample training after feature extraction;
6) gather facial image by video camera or scanner;
7) facial image of gathering being carried out detection and location, whether in image have people face, if people's face is arranged, then make accurate in locating if judging;
8) facial image after the step 7) detection and location is carried out pre-service;
9) pretreated facial image in the step 8) is carried out feature extraction;
10) projection coefficient of facial image sample set in the projection coefficient of facial image to be identified in the step 9) and the database is compared the output recognition result.
In a preferred embodiment of the present invention, comprise that further the pretreated step of step 3) and step 8) is:
A) background in the deletion facial image is found out people's face in the position of image;
B) brightness of adjustment facial image.
In a preferred embodiment of the present invention, the pre-service that further comprises step 3) and step 8) is to set up the faceform with 3D shape and texture according to 3D people's shape of face varying model method.
In a preferred embodiment of the present invention, further comprise and adopt the PCA algorithm to extract face characteristic in step 4) and the step 9).
The invention solves the defective that exists in the background technology, the 3D face identification method based on PCA of the present invention utilizes improved PCA algorithm to image recognition, has improved recognition efficiency; Adopt 3D people's shape of face varying model method to set up 3D faceform's method, improved the accuracy of identification of people's face.
Description of drawings
The present invention is further described below in conjunction with drawings and Examples.
Fig. 1 is the operational flowchart of the 3D face identification method based on PCA of the present invention.
Embodiment
The present invention is further detailed explanation in conjunction with the accompanying drawings and embodiments now, and these accompanying drawings are the synoptic diagram of simplification, basic structure of the present invention only is described in a schematic way, so it only shows the formation relevant with the present invention.
As shown in Figure 1, a kind of 3D face identification method based on PCA is characterized in that: comprise following steps:
S1, establishment have the database of facial image sample; If in actual applications, the facial image sample in the database may have only one or two, will cause BP train samples shortage in the subsequent step like this, so the database of abundant people's face object samples should be arranged in application.
S2, the facial image sample in the database being carried out detection and location, whether in image have people face, if people's face is arranged, then make accurate in locating if judging.
S3, the facial image sample after the detection and location is carried out pre-service.
S4, pretreated facial image sample is carried out feature extraction, adopt the PCA algorithm that the facial image sample is carried out feature extraction.
S5, utilize the BP neural network to the facial image sample training after feature extraction; The BP neural network is a kind of multilayer feedforward network of one way propagation.The BP learning algorithm is called " error Back-Propagation algorithm ", and basic thought is to adjust weight distribution by the minimal value of network error function to make neural network converge on steady state (SS), thereby makes network also can provide suitable output when accepting unknown input.
S6, gather facial image by video camera or scanner.
S7, the facial image of gathering being carried out detection and location, whether in image have people face, if people's face is arranged, then make accurate in locating if judging.
S8, the facial image after the step S7 detection and location is carried out pre-service.
S9, pretreated facial image among the step S8 is carried out feature extraction; Because the quantity of information of people's face picture that video camera or scanner are gathered is often very big, directly handle to produce huge calculated amount, so before recognition of face, will carry out feature extraction.In the dimension that reduces feature space, keep identifying information as much as possible, to reach effective classification.Feature extraction in this step adopts the PCA algorithm that the facial image sample is carried out feature extraction.
S10, the projection coefficient of facial image sample set in the projection coefficient of facial image to be identified among the step S9 and the database is compared the output recognition result.
Adopt the PCA algorithm that the facial image sample is carried out feature extraction among the present invention, this basic idea is: image vector is converted to low dimensional vector through after the Karhunen-Loeve transformation by high dimension vector, and formation low-dimensional linear vector space, it is proper subspace, then people's face is projected to this lower dimensional space, with the proper vector of resulting projection coefficient as identification.During identification people face, only need the projection coefficient of target sample collection in the projection coefficient of sample to be identified and the database is compared, to determine and which kind of is nearest.
The PCA algorithm, has identification accurately and the little advantage of calculated amount, but under following some situation, has defective preferably under the situation in picture quality to be identified, and first image to be identified and training image illuminance difference are bigger; It two is that people's face background difference is bigger.For improving the PCA algorithm, can do pre-service to image, in step S3 and S8, pre-service comprises two steps:
A, deletion background: the deletion background will be found out people's face in the position of image, to this, face identification method of the present invention is found out the position of people's face by the method for 3D deformation model modeling, and further find out eye position, calculate people's general scope of being bold according to the distance between two eyes then, suitable adjustment by to people's face scope makes its normalization.
The brightness of b, every facial image of adjustment: by setting a fixed value, adjust the gray-scale value of image pixel, make gradation of image mean value reach this fixed value, by homomorphic filtering, reduce uneven illumination.
Wherein, the pre-service of step S3 and step S8 is to set up the faceform with 3D shape and texture according to 3D people's shape of face varying model method, and the core concept of 3D people's shape of face varying model is to utilize the 3D faceform's of limited quantity linear combination to express shape and the texture of any one 3D people's face.
3D face identification method based on PCA of the present invention, the principal component analysis (PCA) that utilizes (principal compONent analysis, PCA) be a kind of method that is used for analyzing data in the multivariate statistical analysis, it is the method that with a kind of feature of lesser amt sample is described to reach reduction feature space dimension, the basis of method is the Karhunen-Loeve expansion, the great advantage of K-L conversion is that decorrelation is good, a large amount of irrelevant redundant informations in the image can be removed like this, reduce the structure complexity of BP neural network, also improved training effectiveness and the convergency factor of neural network simultaneously.
3D face identification method based on PCA of the present invention utilizes improved PCA algorithm to image recognition, has improved recognition efficiency; Adopt 3D people's shape of face varying model method to set up 3D faceform's method, improved the accuracy of identification of people's face.
Above foundation desirable embodiment of the present invention is enlightenment, and by above-mentioned description, the related personnel can carry out various change and modification fully in the scope that does not depart from this invention technological thought.The technical scope of this invention is not limited to the content on the instructions, must determine technical scope according to the claim scope.
Claims (4)
1. 3D face identification method based on PCA is characterized in that: comprise following steps:
1) creates the database with facial image sample;
2) the facial image sample in the database being carried out detection and location, whether in image have people face, if people's face is arranged, then make accurate in locating if judging;
3) the facial image sample after the detection and location is carried out pre-service;
4) pretreated facial image sample is carried out feature extraction;
5) utilize the BP neural network to the facial image sample training after feature extraction;
6) gather facial image by video camera or scanner;
7) facial image of gathering being carried out detection and location, whether in image have people face, if people's face is arranged, then make accurate in locating if judging;
8) facial image after the step 7) detection and location is carried out pre-service;
9) pretreated facial image in the step 8) is carried out feature extraction;
10) projection coefficient of facial image sample set in the projection coefficient of facial image to be identified in the step 9) and the database is compared the output recognition result.
2. the 3D face identification method based on PCA according to claim 1, it is characterized in that: the pretreated step of step 3) and step 8) is:
A) background in the deletion facial image is found out people's face in the position of image;
B) brightness of adjustment facial image.
3. the 3D face identification method based on PCA according to claim 2 is characterized in that: the pre-service of step 3) and step 8) is to set up the faceform with 3D shape and texture according to 3D people's shape of face varying model method.
4. the 3D face identification method based on PCA according to claim 1 is characterized in that: adopt the PCA algorithm to extract face characteristic in step 4) and the step 9).
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| CN105117692A (en) * | 2015-08-05 | 2015-12-02 | 福州瑞芯微电子股份有限公司 | Real-time face identification method and system based on deep learning |
| CN106841212A (en) * | 2016-12-30 | 2017-06-13 | 湖南大学 | A kind of bottle mouth defect detection method based on local PCA and BP neural network |
| CN107423678A (en) * | 2017-05-27 | 2017-12-01 | 电子科技大学 | A kind of training method and face identification method of the convolutional neural networks for extracting feature |
| CN109993125A (en) * | 2019-04-03 | 2019-07-09 | 腾讯科技（深圳）有限公司 | Model training method, face identification method, device, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104573641A (en) * | 2014-12-26 | 2015-04-29 | 苏州福丰科技有限公司 | Face recognition method under illumination change condition |
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| CN106841212A (en) * | 2016-12-30 | 2017-06-13 | 湖南大学 | A kind of bottle mouth defect detection method based on local PCA and BP neural network |
| CN106841212B (en) * | 2016-12-30 | 2019-06-21 | 湖南大学 | A kind of bottle mouth defect detection method based on local PCA and BP neural network |
| CN107423678A (en) * | 2017-05-27 | 2017-12-01 | 电子科技大学 | A kind of training method and face identification method of the convolutional neural networks for extracting feature |
| CN109993125A (en) * | 2019-04-03 | 2019-07-09 | 腾讯科技（深圳）有限公司 | Model training method, face identification method, device, equipment and storage medium |
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Application publication date: 20130717 |