Seeing the unseen: a novel approach to biometric recognition systems

This paper introduces an innovative three-phase cascade framework designed for biometric recognition systems, particularly suited for small-scale applications. By integrating multiple biometric modalities - dorsal vein, wrist vein, and palm print - the framework aims to improve recognition accuracy and robustness. The first phase focuses on extracting unique features from each modality using a moment-based approach that is transformation-invariant and computationally efficient. In the second phase, an asymmetric aggregator operator is employed to merge these features into a unified representation. The final phase utilizes spectral clustering to classify and match the fused feature vectors, effectively addressing unseen samples. Evaluated on 350 samples from the COEP and FYO benchmark databases, the framework achieved an impressive accuracy of around 98% for unseen samples, outperforming existing methods like Zernike moment and hierarchical clustering. This work not only enhances biometric authentication but also broadens its applicability across various domains, marking a significant advancement in the field.