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Acquiring Non Linear Subspace for Face Recognition using Symbolic Kernel PCA Method

In this paper, a new technique called symbolic kernel Principal Component Analysis (KPCA) is explored to develop a model for face representation and recognition. The conventional kernel PCA method extracts single valued features from the original image space to represent face images. The proposed method reduces the dimensionality of original image space by representing the face images as symbolic objects (symbolic faces) of interval type variables. Then symbolic kernel PCA method is employed to compute non-linear interval type features from symbolic faces. These features form the non-linear subspace, which maximally preserve original face image information with reduced dimension. A minimum distance classifier with symbolic dissimilarity measure is used for classification. We compare the recognition results of proposed symbolic kernel PCA method with the eigenface method and conventional kernel PCA method using ORL database. Experimental results show that symbolic kernel PCA method with polynomial kernel of degree three achieves improved recognition rate.