CNN-based anti-spoofing two-tier multi-factor authentication system

Many hybrid and multimodal biometric recognition techniques have been presented to provide secure and authentic systems, incorporating both soft and hard biometric schemes. This article proposes a new hybrid technique which ensures the authenticity of the user to the system, as well as monitors whether the user has passed the biometric system as a normal or spoofed one. The proposed scheme is two-fold: Tier I integrates fingerprint, palm vein print and face recognition to match with the corresponding databases, and Tier II uses fingerprint, palm vein print and face anti-spoofing convolutional neural networks (CNN) based models to detect spoofing. In first stage, the hash of a fingerprint is compared with the fingerprint database. After a successful match of the fingerprint, it is tested on a CNN-based model of the fingerprint to verify whether it is a spoof or real. A similar process is repeated for the palm and face, and based on collective evidence, the system permits the user to login the system. Experimental results over five benchmark datasets verified the effectiveness of the proposed system in providing efficient and robust verification, overcoming the limitations in normal authentication and spoofing practices.