Face recognition method based on neighbor preserving canonical correlation analysis

A typical correlation analysis and neighbor keeping technology, applied in the field of classification and recognition, can solve the problems affecting the recognition speed, the large amount of face image data, and the lack of use of face label information, etc., to improve the ability and stability of face recognition, Improving the discrimination ability and maintaining the effect of the neighborhood structure

Pending Publication Date: 2020-09-01
YANGZHOU UNIV
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Problems solved by technology

The traditional CCA face recognition method is an unsupervised method, which does not make good use of the label inf...

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  • Face recognition method based on neighbor preserving canonical correlation analysis
  • Face recognition method based on neighbor preserving canonical correlation analysis
  • Face recognition method based on neighbor preserving canonical correlation analysis

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Embodiment Construction

[0041] Such as figure 1 Shown is a face recognition method based on neighbor-preserving canonical correlation analysis, comprising the following steps:

[0042] Step 1: Input face training dataset X∈R m×N , Y∈R n×N , through neighbor-preserving learning to calculate the image's neighbor weight reconstruction matrix U x and U y ;

[0043] Input face training data X=[x 1 ,x 2 ,...,x N ]∈R m×N and Y=[y 1 ,y 2 ,...,y N ]∈R n×N , to calculate the k-nearest neighbor reconstruction weight matrix U of the training samples x and U y , U x and U y It can be obtained by minimizing the following objective function:

[0044]

[0045]

[0046] with

[0047]

[0048]

[0049] in with Respectively represent face samples x i and y i The k-nearest neighbor samples are calculated to get U x =(u x,ij ) and U y =(u y,ij ).

[0050] Step 2: Use canonical correlation analysis to find two sets of projection vectors w x and w y , use the optimization method to...

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Abstract

The invention discloses a face recognition method based on neighbor preserving canonical correlation analysis, and the method comprises the following steps: 1, inputting a face training data set X belonging to Rm*N, Y belonging to Rn*N, and calculating neighbor weight reconstruction matrixes Ux and Uy of an image through neighbor preserving learning; 2, searching two groups of projection vectors wx and wy by adopting canonical correlation analysis, introducing neighbor preserving into a canonical correlation analysis framework by adopting an optimization method, and calculating projection matrixes Wx and Wy by utilizing generalized eigenvalue decomposition; 3, performing low-dimensional projection and fusion on the test face image by adopting two feature fusion strategies; and 4, applyingthe fused features to face recognition by using a nearest neighbor classifier. According to the invention, a face proximity weight reconstruction matrix is learned through neighbor preserving; neighbor preserving is introduced into a typical correlation analysis framework by using an optimization method, and label information of human faces is utilized, so that the extracted human face features not only maximize the correlation between different human faces, but also maintain the neighborhood structure of the human faces as much as possible, and the human face recognition capability and stability are improved.

Description

technical field [0001] The invention relates to the field of classification recognition in machine learning, in particular to a face recognition method based on neighbor-preserving canonical correlation analysis. Background technique [0002] With the rapid development of modern information technology, the technology for identity authentication has transferred to the biometric level. Modern biometric technology is mainly through the close combination of computer and high-tech means, using the inherent physiological characteristics and behavioral characteristics of the human body to identify personal identity. Among them, face recognition refers to the distribution of human facial features and contours. These distribution characteristics vary from person to person and are inherent. Face recognition is a technology based on human facial feature information. Use a camera or camera to collect images or video streams containing faces, and automatically detect and track faces in...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/168G06V40/172G06F18/24147G06F18/253G06F18/214
Inventor 袁运浩张超张晖李云强继朋李斌
Owner YANGZHOU UNIV
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