Class-Specific Kernel-Discriminant Analysis for Face Verification

In this paper, novel nonlinear subspace methods for face verification are proposed. The problem of face verification is considered as a two-class problem (genuine versus impostor class). The typical Fisher's linear discriminant analysis (FLDA) gives only one or two projections in a two-class problem. This is a very strict limitation to the search of discriminant dimensions. As for the FLDA for
N
class problems (
N
is greater than two), the transformation is not person specific. In order to remedy these limitations of FLDA, exploit the individuality of human faces and take into consideration the fact that the distribution of facial images, under different viewpoints, illumination variations, and facial expression is highly complex and nonlinear, novel kernel-discriminant algorithms are proposed. The new methods are tested in the face verification problem using the XM2VTS, AR, ORL, Yale, and UMIST databases where it is verified that they outperform other commonly used kernel approaches such as kernel-PCA (KPCA), kernel direct discriminant analysis (KDDA), complete kernel Fisher's discriminant analysis (CKFDA), the two-class KDDA, CKFDA, and other two-class and multiclass variants of kernel-discriminant analysis based on Fisher's criterion.