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

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...

AI Technical Summary

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 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 difficulty in face recognition.

[0004] The problem of low-resolution face recognition has greatly challenged traditional face recognition methods due to the low quality of face images and mismatched dimensions

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