Multimodal Biometric-Based Authentication with Secured Templates

Abstract

This paper proposes a multimodal biometric based authentication (verification and identification) with secured templates. Multimodal biometric systems provide improved authentication rate over unimodal systems at the cost of increased concern for memory requirement and template security. The proposed framework performs person authentication using face and fingerprint. Biometric templates are protected by hiding fingerprint into face at secret locations, through blind and key-based watermarking. Face features are extracted from approximation sub-band of Discrete Wavelet Transform, which reduces the overall working plane. The proposed method also shows high robustness of biometric templates against common channel attacks. Verification and identification performances are evaluated using two chimeric and one real multimodal dataset. The same systems, working with compressed templates provides considerable reduction in overall memory requirement with negligible loss of authentication accuracies. Thus, the proposed framework offers positive balance between authentication performance, template robustness and memory resource utilization.