Performance Analysis of Multimodal Biometric System Authentication

Traditional identity verification in computer systems are done based on Knowledge based and token based identification these are prone to fraud. Unfortunately, these may often be forgotten, disclosed or changed. A reliable and accurate identification/verification technique may be designed using biometric technologies. Biometric authentication employs unique combinations of measurable physical characteristics-fingerprint, facial features, iris of the eye, voice print, hand geometry, vein patterns, and so on--that cannot be readily imitated or forged by others. Unimodal biometric systems have variety of problems such as noisy data, intra-class variations, restricted degree of freedom, non-universality, spoof attacks, and unacceptable error rates. Multimodal biometrics refers the combination of two or more biometric modalities in a single identification system. The purpose of this paper is to identify whether the integration of iris and fingerprint biometrics overcome the hurdles of unimodal biometric system. 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 fingerprint, the level of fusion (multimodal fusion) is applied to that are possible and the integration strategies that can be adopted in order to increase the overall system performance. Information from multiple sources can be consolidated in three distinct levels [1]: (i) feature extraction level; (ii) match score level; and (iii) measurement level, (iv) decision level.