Decision level fusion framework for face authentication system

Abstract

In this paper, multiple algorithm and score-level fusion for enhancing the performance of the face based biometric person authentication system is proposed. Though many algorithms are conferred, several crucial issues are still involved in the face authentication. Most traditional algorithms are based on certain assumptions failing which the system will not give appropriate results. Due to the inherent variations in face with time and space, it is a big challenge to formulate a single algorithm based on the face biometric that works well under all variations. This paper addresses the problem of illumination and pose variations, by using three different algorithms for face recognition: Block Independent Component Analysis (B-ICA), Discrete Cosine Transform (DCT) and Kalman filter. The weighted average based score level fusion is performed to improve the results obtained by the system. An intensive analysis of the various algorithms has been performed and the results indicate an increase in accuracy of the proposed system. © 2012 Springer Science+Business Media, LLC.