Doubly weighted nonnegative matrix factorization for imbalanced face recognition

Authors:
Jiwen Lu; Yap-Peng Tan
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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A doubly weighted approach for appearance-based subspace learning methods

IEEE Transactions on Information Forensics and Security

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Abstract

We propose in this paper a novel doubly weighted nonnegative matrix factorization (DWNMF) method for imbalanced face recognition. Motivated by the fact that some face samples and certain parts of each face sample are more useful for recognition, we construct two weighted matrices based on the pairwise similarity of face samples in the same class and the discriminant score of each face pixel. Compared with the existing NMF algorithm, the proposed DWNMF method can more effectively exploit the discriminative and geometrical information of face samples, and it is especially suitable for imbalanced face recognition. Experimental results are presented to demonstrate the efficacy of the proposed method.