Face recognition based on semi-supervised dimensionality reduction and sparse representation
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1.
College of Electronic and Information Engineering, Hebei University, Baoding 071002, China;
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2.
Computer Center, Hebei University, Baoding 071002, China
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Received Date:
2014-01-02
Accepted Date:
2014-02-28
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Abstract
Because of high dimensions of face image data and large calculation of sparse representation classification for face recognition, in order to improve the efficiency of face recognition system, a new face recognition method based on semi-supervised dimensionality reduction(SSDR) and sparse representation (SR) was proposed. Firstly, SSDR algorithm was used to reduce the image dimensions and achieve higher recognition rate in the lower dimension space quickly. Secondly, SR classification can achieve a higher recognition rate than the nearest neighbor classification in face recognition. And then, the experimental verification was demonstrated on ORL face database. The results show that the fusion algorithm can improve the recognition performance of face images quickly and effectively.
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