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Nearest-neighbor classifier motivated marginal discriminant projections for face recognition

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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.

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China

    Pu Huang, Zhenmin Tang & Xintian Cheng

  2. College of Information Engineering, Yangzhou University, Yangzhou, 225009, China

    Caikou Chen

Authors
  1. Pu Huang
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  2. Zhenmin Tang
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  3. Caikou Chen
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  4. Xintian Cheng
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Corresponding author

Correspondence to Pu Huang.

Additional information

Pu Huang received his BSc and MSc in computer applications from Yangzhou University, China, in 2007 and 2010, respectively. He is currently pursuing a PhD in pattern recognition and intelligent systems at Nanjing University of Science and Technology, China. His research interests include pattern recognition, computer vision and machine learning.

Zhenmin Tang received his BSc in computer software from Harbin Engineering University in 1982, his MSc in computer applications and his PhD in pattern recognition and intelligent systems from Nanjing University of Science and Technology (NUST), China, in 1988 and 2002, respectively. He is currently a professor in the School of Computer Science and Technology, NUST. His current interests are in the areas of pattern recognition, computer vision, machine learning and intelligent robots.

Caikou Chen obtained his BSc and MSc in 1991 and 2000, respectively, and his PhD at the Nanjing University of Science and Technology (NUST) in the Department of Computer Science on the subject of pattern recognition and intelligence systems in 2004. From 2005 to 2007, he was a postdoctoral researcher at NUST. From 2009 to 2010, he was a visiting professor at the Robotics Institute of Carneige Mellon University. Now, he is a professor in the College of Information Engineering of Yangzhou University. He is the author of more than 30 scientific papers in pattern recognition and computer vision. His current research interests include pattern recognition, computer vision and machine learning.

Xintian Cheng received her BSc in computer science and technology and her MSc in computer software and theory from Henan University of Technology, China, in 2007 and 2010, respectively. She is currently pursuing her PhD in pattern recognition and intelligent systems at Nanjing University of Science and Technology, China. Her research interests include pattern recognition, computer vision and machine learning.

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Huang, P., Tang, Z., Chen, C. et al. Nearest-neighbor classifier motivated marginal discriminant projections for face recognition. Front. Comput. Sci. China 5, 419–428 (2011). https://doi.org/10.1007/s11704-011-1012-z

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  • DOI: https://doi.org/10.1007/s11704-011-1012-z

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