Incremental and Decremental Nonparametric Discriminant Analysis for Face Recognition

Authors

  • Nitin Kumar Department of Computer Science and Engineering, NIT Uttarakhand, India
  • Ramesh Kumar Agrawal School of Computer Science and System Sciences, Jawaharlal Nehru University New Delhi
  • Ajay Jaiswal S. S. College of Business Studies, University of Delhi, New Delhi

Keywords:

Small sample size, linear discriminant analysis, nonparametric discriminant analysis, scatter matrix, face recognition

Abstract

Nonparametric Discriminant Analysis (NDA) possesses inherent advantages over Linear Discriminant Analysis (LDA) such as capturing the boundary structure of samples and avoiding matrix inversion. In this paper, we present a novel method for constructing an updated Nonparametric Discriminant Analysis (NDA) model for face recognition. The proposed method is applicable to scenarios where bursts of data samples are added to the existing model in random chunks. Also, the samples which degrade the performance of the model need to be removed. For both of these problems, we propose incremental NDA (INDA) and decremental NDA (DNDA) respectively. Experimental results on four publicly available datasets viz. AR, PIE, ORL and Yale show the efficacy of the proposed method. Also, the proposed method requires less computation time in comparison to batch NDA which makes it suitable for real time applications.

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Author Biography

Nitin Kumar, Department of Computer Science and Engineering, NIT Uttarakhand, India

Assistant Professor, National Institute of Technology, Uttarakhand

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Published

2017-02-07

How to Cite

Kumar, N., Agrawal, R. K., & Jaiswal, A. (2017). Incremental and Decremental Nonparametric Discriminant Analysis for Face Recognition. Computing and Informatics, 35(5), 1231–1248. Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/1832