Determination of Feature Hierarchy from Gabor and SIFT Features for Face Recognition

This paper shows how the most important features can be selected from the face so that the performance of any face recognition engine can be improved by matching only the maximally distinguishable features. Creating an automated face recognition system that can duplicate human performance in recognizing a face is one of the key goal of computer vision researchers. So, it is necessary that computational researchers should know the key findings from a facial image. Here the feature hierarchy in accordance with importance to recognize a face is used in our Face Recognition system and it is observed that the performance have been improved drastically after selecting the mostly contributing feature set.