Authors: Research Scholar Akhilesh Singh, Associate Professor Dr Namita Tiwari, Associate Professor Dr Mayur Rahul
Abstract: With an increasingly large number of online services and secure access applications, trusted identity authentication has become an important issue. Although biometric authentication has higher security assurance than traditional security methods, single biometric mode authentication systems have performance issues in terms of degradation due to environmental factors, occlusions, lighting, and spoofing attacks. In this respect, this study proposes an original multimodal biometric authentication approach that combines ear contour biometric recognition with palmprint biometric recognition using the Enhanced and Discriminative Competitive Code (EDCC) method. The proposed multimodal biometric authentication method has the synergistic ability of two biometric modes. The ear contour-based biometric recognition technique extracts the helix and conchal curvatures of the human ear, providing geometric information that is less affected by illumination conditions. Simultaneously, the EDCC-based palmprint recognition technique extracts the prevalent orientation patterns of the lines and ridges on the human palm, providing robustness to noise and minute geometric distortions. These two biometric modalities provide complementary information about the user’s biometric traits and can thus be fused through feature-level fusion to provide a single and robust biometric representation.The performance of the proposed multimodal biometric authentication technique is evaluated on two challenging and widely available biometric datasets, namely the PolyU-IITD contactless palmprint database and the EarVN1.0 unconstrained ear image database. The performance evaluation of the proposed multimodal biometric authentication technique clearly reveals its superior performance compared to other state-of-the-art biometric authentication approaches, including unimodal and hybrid biometric authentication schemes, as it provides a recognition accuracy of 99.01% and an extremely low EER of 0.11% for the PolyU-IITD contactless palmprint database and EarVN1.0 unconstrained ear image database, respectively.
