A novel face recognition system base on nonparametric discriminant analysis

Biometrics serves to identify the identity of an individual with the help of mathematical analysis of its biological features. In this paper, we propose a novel classification method based on kernel Nonparametric Discriminant Analysis (KNDA). We use the facial features extracted with Gabor descriptor and ordinal filter, which are encoded in local regions, as visual primitives. The different ordinal measures are derived from various kinds of components of Gabor images such as magnitude, phase, real and imaginary. Then, the statistical distributions of these primitives in face image blocks are concatenated to obtain a feature vector whose dimension is reduced using PCA and variance. Each feature vector is treated as a feature input for the proposed Multi-class KNDA classifier. The proposed method incorporates some near-global variations of the data provided by the KNDA. In fact, this latter is based on flexible non-linear separation between face database classes. Moreover, it relaxes the normality assumption of the Linear Discriminant Analysis (LDA). The proposed method is tested on the well-known ORL face database and the Yale face database. Then, it is evaluated and compared with non-linear classifier (KFD) and linear classifier (LDA) in term of classification accuracy.