Nearest-neighbor classifier motivated marginal discriminant projections for face recognition

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, k₁ and k₂, 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.

Keywords

dimensionality reduction (DR) / face recognition / marginal Fisher analysis (MFA) / locality preserving projections (LPP) / graph construction / margin-based / nearest-neighbor (NN) classifier

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Higher Education Press and Springer-Verlag Berlin Heidelberg