Nearest-neighbor classifier motivated marginal discriminant projections for face recognition

doi:10.1007/s11704-011-1012-z

Front Comput Sci Chin

2011, Vol. 5

Issue (4) : 419-428 https://doi.org/10.1007/s11704-011-1012-z

RESEARCH ARTICLE

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.

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

Corresponding Author(s): HUANG Pu,Email:huangpu3355@163.com

Issue Date: 05 December 2011

Cite this article:

Pu HUANG,Zhenmin TANG,Caikou CHEN, et al. Nearest-neighbor classifier motivated marginal discriminant projections for face recognition[J]. Front Comput Sci Chin, 2011, 5(4): 419-428.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-011-1012-z
https://academic.hep.com.cn/fcs/EN/Y2011/V5/I4/419

Fig.1 Samples in original space

Fig.2 Samples after projection

Fig.3 10 images of one person in ORL database

Fig.4 11 images of one person in Yale database

Fig.5 7 images of one person in FERET database

Fig.6 Recognition accuracy of LPP corresponding to different with different classifiers

Fig.7 Recognition accuracy of MFA corresponding to different and with (a) NN; (b) SVM

Tab.1 Recognition accuracy, dimension, and consuming time of algorithms (PCA, LDA, LPP, MFA, SMDA and NN-MDP) on ORL database

Tab.2 Recognition accuracy of algorithms (PCA, LDA, LPP, MFA, SMDA and NN-MDP) on Yale database

Tab.3 Recognition accuracy, dimension, and consuming time of algorithms (PCA, LDA, LPP, MFA, SMDA and NN-MDP) on FERET database in Case 1

Tab.4 Recognition accuracy, dimension, and consuming time of algorithms (PCA, LDA, LPP, MFA, SMDA and NN-MDP) on FERET database in Case 2

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