A Bezier Curve Cohort Selection Strategy for Face Pair Matching

The matching of two face images without any prior information is very much challenging task unlike a verification or identification system where already some knowledge about the images of each subjects are stored in the system's database. This paper proposes a methodology to enrich the performance of a face pair matching system by utilizing the complementary information collected from a set of cohort face images with the help of Bezier Curve cohort selection algorithm. A pair of face images is given as input to the system. Each image is compared with a predefined cohort pool to form two separate set of cohort scores. Further these set of cohort scores are passed through Bezier curve cohort selection method which provide two suitable cohort subsets. Afterwards a cross normalization is accomplished in conjunction with T-norm score normalization method then the absolute normalized difference between the paired face images is determined. On the basis of this normalized difference, it is finally decided whether the input face pair is from same person or not. The system is investigated with FEI face database and the results are quite impressive.