Comparing human faces using edge weighted dissimilarity measure

This paper proposes a dissimilarity measure that can be used as a distance between two images. It has shown better discriminative power to recognize faces than similar existing variants for discriminating facial images. It gives more weight to the pixels which are often a part of the edge and counts pixels that are unmatched between query and database images. This measure has been tested on a publicly available database as ORL, YALE, CALTEC, BERN and also on a database developed at IIT Kanpur. Experimental results show that the proposed measure achieves a high recognition rate of 99.75% 93.75% 99.03% 98.93% 99.73% for the first likely matched faces on databases ORL, YALE, BERN, CALTECH, IITK respectively. The proposed measure can provide not only effective result against pose and expression variations but also against slight illumination variation.