Recognizing Partially Damaged Facial Images by Subspace Auto-associative Memories
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, PCA LDA and NMF LDA , 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 NMF LDA subspace associative memory outperform PCA LDA subspace method significantly in recognizing partially damaged faces.
KeywordsFace Recognition Linear Discriminant Analysis Facial Image Positive Matrix Factorization Sparseness Constraint
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