Selection of User-Dependent Cohorts Using Bezier Curve for Person Identification

Abstract

The traditional biometric systems can be strengthened further with exploiting the concept of cohort selection to achieve the high demands of the organizations for a robust automated person identification system. To accomplish this task the researchers are being motivated towards developing robust biometric systems using cohort selection. This paper proposes a novel user-dependent cohort selection method using Bezier curve. It makes use of invariant SIFT descriptor to generate matching pair points between a pair of face images. Further for each subject, considering all the imposter scores as control points, a Bezier curve of degree n is plotted by applying De Casteljau algorithm. As long as the imposter scores represent the control points in the curve, a cohort subset is formed by considering the points determined to be far from the Bezier curve. In order to obtain the normalized cohort scores, T-norm cohort normalization technique is applied. The normalized scores are then used in recognition. The experiment is conducted on FEI face database. This novel cohort selection method achieves superior performance that validates its efficiency.