Methods for improving discriminant analysis for face authentication
A novel algorithm that can be used to boost the performance of face authentication methods that utilize Fisher's criterion is presented. The algorithm is applied to matching error data and provides a general solution for overcoming the "small sample size" (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of locally linear discriminant models is used in order to calculate discriminant weights in a more accurate way than the traditional linear discriminant analysis (LDA) methodology. Additionally, defective discriminant coefficients are identified and reestimated. The second phase defines proper combinations for person-specific matching scores and describes an outlier removal process that enhances the classification ability. Our technique was tested on the M2VTS and XM2VTS frontal face databases. Experimental results indicate that the proposed framework greatly improves the authentication algorithm's performance.