Performance Evaluation of Multimodal Biometric System using Fusion of Iris and Face

Abstract

Unimodal biometric systems have to contend with a variety of problems such as noisy data, intraclass variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. Some of these limitations can be addressed by deploying multimodal biometric systems that integrate the evidence presented by multiple sources of information. Fusion of multiple biometrics for human authentication performance improvement has received considerable attention. This paper presents a novel multimodal biometric authentication method integrating face and iris based on score level fusion. For score level fusion, support vector machine (SVM) based fusion rule is applied to combine two matching scores, respectively from Laplacian face based face verifier and phase information based iris verifier, to generate a single scalar score which is used to make the final decision. Experimental results show that the performance of the proposed method can bring obvious improvement comparing to the unimodal biometric identification methods and the previous fused face−iris methods. This paper discusses the various scenarios that are possible to improve the performance of multimodal biometric systems using the combined characteristics such as iris and face, the level of fusion (score level fusion) is applied to that are possible and the integration strategies that can be adopted in order to increase the overall system performance.