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Nearest-neighbor classifier motivated marginal discriminant projections for face recognition |
Pu HUANG1( ), Zhenmin TANG1, Caikou CHEN2, Xintian CHENG1 |
1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China; 2. College of Information Engineering, Yangzhou University, Yangzhou 225009, China |
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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, 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.
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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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Corresponding Author(s):
HUANG Pu,Email:huangpu3355@163.com
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Issue Date: 05 December 2011
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