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International Journal of Engineering، جلد ۳۱، شماره ۱۰، صفحات ۱۶۹۸-۱۷۰۷
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عنوان انگلیسی Iterative Weighted Non-smooth Non-negative Matrix Factorization for Face Recognition
چکیده انگلیسی مقاله Non-negative Matrix Factorization (NMF) is a part-based image representation method. It comes from the intuitive idea that entire face image can be constructed by combining several parts. In this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “Iterative weighted non-smooth non-negative matrix factorization” (IWNS-NMF). A new cost function is proposed in order to incorporate sparsity which is controlled by a specific parameter and weights of feature coefficients. This method extracts highly localized patterns, which generally improves the capability of face recognition. After extracting patterns by IWNS-NMF, we use principle component analysis to reduce dimension for classification by linear SVM. The Recognition rates on ORL, YALE and JAFFE datasets were 97.5, 93.33 and 87.8%, respectively. Comparisons to the related methods in the literature indicate that the proposed IWNS-NMF method achieves higher face recognition performance than NMF, NS-NMF, Local NMF and SNMF.
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نویسندگان مقاله B. Sabzalian |
Faculty of Electrical Engineering and Robotics, Shahrood University of Technology, Shahrood, Iran

V. Abolghasemi |
Faculty of Electrical Engineering and Robotics, Shahrood University of Technology, Shahrood, Iran

نشانی اینترنتی http://www.ije.ir/article_82211_e644008323238e0fe979493df18d8816.pdf
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