An Experimental Study of Different Features for Face Recognition

Abstract

As a first study, the use the Gabor filter bank is made to generate features for face recognition. The features so obtained on the application of SVM classifier yields accuracy rate of 96.2%. With a view to improve the performance, two more feature types, viz., wavelet features and wavelet-fuzzy features resulting from the application of 2D wavelet transform on the Composite detail images and the Approximate images at 3 levels of decomposition, are devised. The ROCs of three feature types show that wavelet-fuzzy features have a better performance. The performance of Gabor features is slightly inferior to that of wavelet-fuzzy features. The algorithm was tested on ORL (Olivetti Research Laboratory) database that has slight orientations in face images.