Low-resolution human face recognition method based on sparse maintaining canonical correlation analysis

A canonical correlation analysis and low-resolution technology, applied in character and pattern recognition, instruments, computing, etc., can solve problems such as dimension mismatch, low quality of face image, complex face recognition, etc., to improve robustness , Enhance feature representation and identification capabilities, meet the requirements of correlation and maintain structural information

Inactive Publication Date: 2016-12-07
SHANDONG UNIV
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Problems solved by technology

However, face recognition is an extremely complex problem. The difficulty lies in the variability of the pattern. The main influencing factors are internal factors such as age, skin color, and expression, environmental factors such as illumination and occlusion, and changes caused by video acquisition equipment.
At present, in order to overcome the influence of internal factors and en

Method used

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  • Low-resolution human face recognition method based on sparse maintaining canonical correlation analysis
  • Low-resolution human face recognition method based on sparse maintaining canonical correlation analysis
  • Low-resolution human face recognition method based on sparse maintaining canonical correlation analysis

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[0057] A Low-Resolution Face Recognition Method Based on Sparse Preserving Canonical Correlation Analysis, Attached figure 1 Shown, including training part and testing part;

[0058] The training part includes steps as follows:

[0059] First, the effective features of the high-resolution face image and the low-resolution face image are extracted respectively through principal component analysis, and the principal component analysis projection matrices corresponding to the high-resolution face image and the low-resolution face image are respectively obtained;

[0060] Then, construct a sparse reconstruction weight matrix to minimize the sparse reconstruction error, learn a set of linear transformation matrices to maximize the correlation between high-resolution face images and low-resolution face image data, and use the low-resolution training sample set and the high-resolution training sample set are projected into a common subspace;

[0061] The test part includes steps as...

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Abstract

The invention provides a low-resolution human face recognition method based on sparse maintaining canonical correlation analysis. The invention combines with the sparsity and canonical correlation analysis idea, and proposes the low-resolution human face recognition method based on sparse maintaining canonical correlation analysis. The method meets the requirements of maximum correlation of the extracted features through employing the canonical correlation analysis idea, achieves the fusion of the high and low resolution human face feature discrimination information, employs the sparsity idea to maintain the structural information, and improves the robustness of high and low resolution human face recognition. The method achieves the effective fusion of the high and low resolution human face feature discrimination information, improves the feature representation and discrimination capability, and meets the requirements of correlation and structural information maintaining.

Description

technical field [0001] The invention relates to a low-resolution face recognition method based on sparse-preserving canonical correlation analysis, which belongs to the technical field of computer vision and pattern recognition. Background technique [0002] Face recognition technology has developed rapidly in recent decades, especially in controllable scenarios, face recognition technology has begun to be applied. However, face recognition is an extremely complex problem. The difficulty lies in the variability of the pattern. The main influencing factors are internal factors such as age, skin color, and expression, environmental factors such as illumination and occlusion, and changes caused by video acquisition equipment. . At present, in order to overcome the influence of internal factors and environmental factors of the face, researchers have designed various algorithms and achieved good results; however, the problem of low resolution has always been an insurmountable di...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V40/161G06V40/168G06V40/172
Inventor 贲晛烨张鹏张振月刘吉松张振卿王云静
Owner SHANDONG UNIV
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