NDLI: An Illumination Robust Algorithm for Face Recognition Via SRC and Gradientfaces

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An Illumination Robust Algorithm for Face Recognition Via SRC and Gradientfaces

Content Provider	ACM Digital Library
Author	Wu, Zhendong Yu, Zipeng Zhang, Jianwu
Abstract	As the theory of Compressive Sensing being put forward, people began to study how to apply CS for face recognition. Allen Y. Yang and John Wright proposed a new method based on CS, named Sparse Representation-based Classification (SRC). SRC cast the recognition problem as one of finding a sparse representation of the test image in terms of the training set as a whole. SRC addressed the recognition problem with illumination invariant images successfully. However, SRC didn't consider the variable illumination and the complexity of calculation. In this paper, we propose an illumination robust algorithm based on SRC, named G_SRC. Moreover, our algorithm is faster than SRC. G_SRC applies the Gradientfaces to preprocess face images and extract illumination invariant. Then, G_SRC applies PCA to extract face feature and reduce the image dimensions. At last, we use SRC to face recognition. G_SRC can weaken the influence of light on the human face and improve the recognition. Experimental results on the ORL and the actual face databases show that G_SRC has better generalization ability than SRC for face recognition.
Starting Page	36
Ending Page	40
Page Count	5
File Format	PDF
ISBN	9781450321198
DOI	10.1145/2556871.2556880
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-12-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Face recognition Src Sparse representation Gradientfaces Pca
Content Type	Text
Resource Type	Article

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