Incremental and Decremental Nonparametric Discriminant Analysis for Face Recognition
Keywords:Small sample size, linear discriminant analysis, nonparametric discriminant analysis, scatter matrix, face recognition
AbstractNonparametric 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.
Download data is not yet available.
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 https://www.cai.sk/ojs/index.php/cai/article/view/1832