Efficient Multimodal Biometric Authentication Using Fast Fingerprint Verification and Enhanced Iris Features

Abstract

Problem statement: The accuracy of biometric systems varies with the kind of biometric feature used in it. The Unmoral biometric system is prone to interclass variations. Approach: We implement Multimodal biometric systems to overcome the limitations by using multiple pieces of evidence of the same identity. However, the multimodal biometric system is limited to the time constraints due to its multiple processing stages. To improve the speed of authentication in the biometric system with acceptable accuracy, we have introduced a dynamic fingerprint verification technique fused with enhanced iris recognition using the adaptive rank level fusion method. Results: When tested upon the standard biometric dataset the system shows improvement in the False Acceptance Rate (FAR) and Equal Error Rate (EER) curves. Essentially, the time taken for the training and verification phase has a reduction of 10% when compared with the existing systems. Conclusion: The multimodel system has necessarily increased the speed and performance of the verification system especially when tested on slow processing and low memory devices.