Complete Kernel Fisher Discriminant Analysis of Gabor Features with Fractional Power Polynomial Models for Face Recognition
Jun-Bao Li, Jeng‐Shyang Pan, Zhe-Ming Lu, Jung-chou Chang · 2006
This paper presents a novel face recognition method based on complete Kernel Fisher discriminant (CKFD) analysis of Gabor features with power polynomial models. By integrating the Gabor wavelet representation of face images and the enhanced powerful discriminator named CKFD analysis, the method is robust to changes in illumination and facial expressions and poses. On the other hand, the extended polynomial Kernels, namely fractional power polynomial (FPP) models, are employed in CKFD analysis, which enhance face recognition performance. Comparing with existing PCA, LDA, KPCA, KFD and CKFD methods, the proposed method gives superior results in the ORL and Yale face databases. Its good performance in the two face databases gives the promising idea to solve the pose, illumination, and expression (PIE) problem of face recognition