Component-based face recognition using statistical pattern matching analysis.

The aim of this research is to develop a fusion concept to component-based face recognition algorithms for features analysis of binary facial components (BFCs), which are invariant to illumination, expression, pose variations and partial occlusion. To analyze the features, using statistical pattern matching concepts, which are the combination of Chi-square (CSQ), Hu moment invariants (HuMIs), absolute difference probability of white pixels (AbsDifPWPs) and geometric distance values (GDVs) have been proposed for face recognition. The individual grayscale face image is cropped by applying the Viola-Jones face detection algorithm from a face database having variations in illumination, appearance, pose and partial occlusion with complex backgrounds.