A Decorrelation-Based Nonnegative Matrix Factorization Algorithm for Face Recognition

Abstract:

Nonnegative Matrix Factorization (NMF) is among the most popular subspace methods, widely used in a variety of image processing problems. However, this approach is very time-consuming in face recognition due to the extreme high dimensionality of the original matrix. To remedy this limitation, this paper presents a Decorrelation-based NMF (DNMF) method. The proposed algorithm first takes into account the dimension reduction of the original matrix by preprocessing of decorrelation in spatial domain, and then uses nearest neighbor classifier on the reduced subspace. The developed algorithm has been applied for the ORL standard face image database. Experimental results demonstrate the validity of this method.

• Periodical: Advanced Materials Research (Volume 651)
• Pages: 858-863
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.651.858
• Online since: January 2013
• Authors: Dan Zhou, Hai Yan Gao, Yun Jie Zhang
• Keywords: Decorrelation in Spatial Domain, Dimension Reduction, Face Recognition, Nonnegative Matrix Factorization

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