Recognizing partially damaged facial images by subspace auto-associative memories

Authors:
Xiaorong Pu;Zhang Yi;Yue Wu
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

PCA and NMF subspace approaches have become the most representative methods in face recognition, which act in the similar way as a neural network auto-associative memory. By integrating with LDA subspace, in this paper, two subspace associative memories, PCALDA and NMFLDA, are proposed, and how they recognize the partially damaged faces is presented. The theoretical expressions are plotted, and the comparative experiments are completed for the UMIST face database. It shows that NMFLDA subspace associative memory outperform PCALDA subspace method significantly in recognizing partially damaged faces.