Discriminative Moment Feature Descriptors for Face Recognition
Geetika Singh, Indu Chhabra · International Journal of Computer Vision and Image Processing · 2015
Zernike Moment (ZM) is a promising technique to extract invariant features for face recognition. It has been modified in previous studies to Discriminative ZM (DZM), which selects most discriminative features to perform recognition, and shows improved results. The present paper proposes modification of DZM, named Modified DZM (MDZM), which selects coefficients based on their discriminative ability by considering extent of variability between their class-averages. This reduces within-class variations while maintaining between-class differences. The study also investigates this idea of feature selection on recently introduced Polar Complex Exponential Transform (PCET) (named discriminative or DPCET). Performance of the techniques is evaluated on ORL, Yale and FERET databases against pose, illumination, expression and noise variations. Accuracy improves up to 3.1% by MDZM at reduced dimensions over ZM and DZM. DPCET shows 1.9% of further improvement at less computational complexity. Performance is also tested on LFW database and compared with many other state-of-art approaches.