近邻样本协作表示的人脸识别算法

doi:10.3969/j.issn.1001-2400.2015.03.020

Abstract

Abstract:

An improved face recognition algorithm using the collaborative representation with nearer neighbors of the testing image is proposed. As a measurement to find the neighboring testing sample,the correlation coefficient between the testing sample and training samples is calculated in the Gabor-feature space. Neighbors of the testing sample compose the compact over-completed dictionary which is variable for different testing samples. The testing image is represented collaboratively by the variable "thickness" compact dictionary and the sparse representation coefficient is calculated with l₂ minimization. The error between the reconstructed image and the testing image categorizes the testing image. This proposed algorithm has been carried out in database of FERET, ORL and AR with variations of lighting, expression, pose, and occlusion. Extensive experiments demonstrate that the proposed approach is superior both in recognition rate and in speed.

Key words: Gabor, correlators, neighbors, collaborative representation, face recognition

CLC Number:

TP391.4

WEI Dongmei;ZHOU Weidong. Face recognition using collaborative representation with neighbors[J].J4, 2015, 42(3): 115-121.

References

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Face recognition using collaborative representation with neighbors

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