DYNAMICS FACES BASED INDEPENDENT COMPONENT ANALYSIS STUDIES FOR BIOMETRICS VERIFICATIONS SYSTEMS APPLICATIONS

Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Dynamics Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level; the fusion within the scores based Likelihood Ration of the classifier. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on eNTERFACE2005 and the simulation results are showed a significant performance achievements.