DESIGN AND DEVELOPMENT OF AN EFFICIENT MULTIMODAL BIOMETRICS BASED RECOGNITION SYSTEM

Abstract

Biometric systems provide various benefits over traditional pin-based authentication systems. Also, Multimodal Biometric Systems are extensively employed over unimodal counterparts for user authentication in the digital world. Here, information from multiple sources is combined to reach a final decision. While, Score level fusion combines outcomes of individual classifiers to make a final decision, feature fusion combines individual features from complimentary biometric traits to generate a superior feature. However, the application of multimodal systems to security-critical applications is limited mainly due to non-adaptiveness of these systems to the dynamic environment and inability to distinguish between spoofing attack and the noisy input image. Further, the issue of data privacy and theft is of great concern. Also, most of the biometric systems suffer from the issue of score confliction of individual classifiers. A multimodal biometric system, which adaptively combines the scores from individual classifiers, is proposed to address these issues. For this, three modalities viz. face, finger, and iris are used to extract individual classifier scores. These classifier scores are adaptively fused considering that concurrent modalities are boosted and discordant modalities are suppressed. The conflicting belief among classifiers is resolved not only to achieve optimum fusion of classifier scores but also to cater dynamic environment. The proposed quality based score fusion also distinguishes between spoofing attacks and noisy inputs as well. The performance of the proposed multimodal biometric system is experimentally validated using three chimeric multimodal databases. A novel cancelable multimodal biometric system is proposed that combines multiple traits by means of a projection-based approach. The proposed approach generates a cancelable vii biometric feature that is used to obtain revocable and noninvertible templates. Cancelable features are generated by projecting the feature points onto a random plane obtained using a user-specific key. The point of projection is then transformed into cylindrical coordinates and a combined cancelable feature is obtained. Extensive experiments are performed over 3 chimeric multimodal databases and results reveal high performance. Also, the proposed method is successfully analyzed for privacy concerns, namely revocability, noninvertibility, and unlinkability. Moreover, the proposed system demonstrated tolerance against various security attacks like brute force attacks, attacks via record multiplicity, and substitution attacks. Also, a novel optimized score level fusion using Grasshopper optimization is proposed where the performance optimization of individual classifiers is performed and a concurrent solution is achieved by means of proportional conflict redistribution rules. The system does not require any classifier training and exhibits high performance. The proposed system is robust against the dynamic environment and exhibits high reliability.