Face Recognition Using Canonical Correlation, Discrimination Power, and Fractional Multiple Exemplar Discriminant Analyses

Face recognition is a biometric identification method which compared to other methods, such as finger print identification, speech, signature, hand written and iris recognition is shown to be more noteworthy both theoretically and practically. Biometric identification methods have various applications such as in film processing, control access networks, among many. The automatic recognition of a human face has become an important problem in pattern recognition, due to (1) the structural similarity of human faces, and (2) great impact of factors such as illumination conditions, facial expression and face orientation. These have made face recognition one of the most challenging problems in pattern recognition. Appearance-based methods are one of the most common methods in face recognition, which can be categorized into linear and nonlinear methods. In this paper face recognition using Canonical Correlation Analysis is introduced, along with the review of the linear and nonlinear appearance-based methods. Canonical Correla- tion Analysis finds the linear combinations between two sets of variables which have maximum correlation with one another. Discriminant Power analysis and Fractional Multiple Discriminant Analysis has been used to extract features from the image. The results provided in this paper show the advantage of this method compared to other methods in this field.