Face Recognition based on Accelerated Joint Boosting and Illumination Normalized Local Gabor Binary Pattern Histogram Sequence

Determining what features are optimal for face representation is quite a challenge in Face Recognition. Joint Boosting is a strong algorithm to select important features from feature pool. And, it outperforms other feature selection methods. But it takes quite a long time to train on a large training set. In this paper, an Accelerated Joint Boosting is proposed to resolve the time wasting problem. The contributions of this paper are: (1) We propose an Accelerated Joint Boosting method for feature selection in Face Recognition. (2) An efficient preprocessing chain for Illumination Normalization which is called ´PP´ and Histogram Sequence of Local Gabor Binary Pattern (LGBPHS) are combined for robust Face Recognition, and it gets remarkable performance in Face Recognition Grand Challenge version 2.0 experiment 4 (FRGC).