Face Recognition Using Symbolic KPCA Plus Symbolic LDA in the Framework of Symbolic Data Analysis: Symbolic Kernel Fisher Discriminant Method

Abstract

In this paper we present a new approach called as symbolic kernel Fisher discriminant analysis (symbolic KFD) for face recognition based on symbolic kernel principal component analysis (symbolic KPCA) and symbolic linear discriminant analysis (symbolic LDA) in the framework of symbolic data analysis. It is well known that the distribution of face images, under a perceivable variation in view point, illumination and facial expression is highly nonlinear and complex. The linear techniques based on symbolic LDA cannot provide reliable and robust solutions to such face recognition problems because these techniques fail to capture a non-linear relationship with linear mapping. However, proposed symbolic KFD method overcomes this limitation by using kernel trick to represent complicated nonlinear relations of input data. The classical KFD method uses single valued variables to represent the facial features, where as, the proposed symbolic KFD extract interval type non linear discriminating features, which are robust due to varying facial expression, view point and illumination. The new algorithm has been successfully tested using three databases, namely, Yale Face database and Yale Face database B. The experimental results show that symbolic KFD outperforms other KFD algorithms.