Nearest-neighbor classifier motivated marginal discriminant projections for face recognition

Authors:
Pu Huang;Zhenmin Tang;Caikou Chen;Xintian Cheng
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China 210094;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China 210094;College of Information Engineering, Yangzhou University, Yangzhou, China 225009;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China 210094
Venue:
Frontiers of Computer Science in China
Year:
2011

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Abstract

Marginal Fisher analysis (MFA) is a representative margin-based learning algorithm for face recognition. A major problem in MFA is how to select appropriate parameters, k 1 and k 2, to construct the respective intrinsic and penalty graphs. In this paper, we propose a novel method called nearest-neighbor (NN) classifier motivated marginal discriminant projections (NN-MDP). Motivated by the NN classifier, NN-MDP seeks a few projection vectors to prevent data samples from being wrongly categorized. Like MFA, NN-MDP can characterize the compactness and separability of samples simultaneously. Moreover, in contrast to MFA, NN-MDP can actively construct the intrinsic graph and penalty graph without unknown parameters. Experimental results on the ORL, Yale, and FERET face databases show that NN-MDP not only avoids the intractability, and high expense of neighborhood parameter selection, but is also more applicable to face recognition with NN classifier than other methods.