FACE RECOGNITION BASED ON GPPBTF AND LBP WITH CLASSIFIER FUSION

Robust face recognition is a challenging problem, due to facial appearance variations in illumination, pose, expression, aging, partial occlusions and other changes. This paper proposes a novel face recognition approach, where face images are represented by Gabor pixel-pattern-based texture feature (GPPBTF) and local binary pattern (LBP), and null pace-based kernel Fisher discriminant analysis (NKFDA) is applied to the two features independently to obtain two recognition results which are eventually combined together for a final identification. To get GPPBTF, we first transform an image into Gabor magnitude maps of different orientations and scales, and then use pixel-pattern-based texture feature to extract texture features from Gabor maps. In order to improve the final performance of the classification, this paper proposes a multiple NKFDA classifiers combination approach. Extensive experiments on FERET face database demonstrate that the proposed method not only greatly reduces the dimensionality of face representation, but also achieves more robust result and higher recognition accuracy.