LBP Discriminant Analysis for Face Verification

Jiali Zhao, Haitao Wang, Haibing Ren, Seok-Cheol Kee

Abstract: This paper presents a novel Local Binary Pattern (LBP) based Kernel Fisher Discriminant Analysis (KFDA) approach by integrating the LBP descriptor of face images and the KFDA method for face classifier. LBP extracts desirable facial features which consider both shape and texture information to cope with the variation due to facial expression and illumination changes. The KFDA method is then extended to take all the advantages of LBP descriptor for improved face verification performance. We introduce the kernel function by using Chi square statistic distance and RBF as inner product for KFDA classifier. The effectiveness of the LBP based KFDA method with Chi square statistic distance as inner product is shown in terms of comparison with original LBP and KFDA methods on FRGC database.