Face Recognition using Symbolic KDA in the Framework of Symbolic Data Analysis

In this paper we present one of the symbolic factor analysis method called as symbolic kernel discriminant analysis (symbolic KDA) for face recognition in the framework of symbolic data analysis. Classical factor analysis methods (specifically classical KDA) extract features, which are single valued in nature to represent face images. These single valued variables may not be able to capture variation of each feature in all the images of same subject; this leads to loss of information. The symbolic KDA Algorithm extracts most discriminating non-linear interval type features; they optimally discriminate among the classes represented in the training set. The proposed method has been successfully tested for face recognition using two databases, ORL and Yale Face database. The effectiveness of the proposed method is shown in terms of comparative performance against popular classical factor analysis methods such as eigenface method and fisherface method. Experimental results show that symbolic KDA outperforms the classical factor analysis methods.

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