Dual Channel Multi-Attention in ViT for Biometric Authentication using Forehead Subcutaneous Vein Pattern and Periocular Pattern

• URL: http://arxiv.org/abs/2412.19160v1
• Date: Thu, 26 Dec 2024 10:40:15 GMT
• Title: Dual Channel Multi-Attention in ViT for Biometric Authentication using Forehead Subcutaneous Vein Pattern and Periocular Pattern
• Authors: Arun K. Sharma, Shubhobrata Bhattacharya, Motahar Reza,
• Abstract summary: This paper proposes a novel dual-channel multi-attention Vision Transformer (ViT) framework for biometric authentication. The proposed framework leverages a dual-channel ViT architecture, designed to handle two distinct biometric traits. The performance of the proposed algorithm was rigorously evaluated using the Forehead Subcutaneous Vein Pattern and Periocular Biometric Pattern database.
• Score: 1.325885579843574
• License:
• Abstract: Traditional biometric systems, like face and fingerprint recognition, have encountered significant setbacks due to wearing face masks and hygiene concerns. To meet the challenges of the partially covered face due to face masks and hygiene concerns of fingerprint recognition, this paper proposes a novel dual-channel multi-attention Vision Transformer (ViT) framework for biometric authentication using forehead subcutaneous vein patterns and periocular patterns, offering a promising alternative to traditional methods, capable of performing well even with face masks and without any physical touch. The proposed framework leverages a dual-channel ViT architecture, designed to handle two distinct biometric traits. It can capture long-range dependencies of independent features from the vein and periocular patterns. A custom classifier is then designed to integrate the independently extracted features, producing a final class prediction. The performance of the proposed algorithm was rigorously evaluated using the Forehead Subcutaneous Vein Pattern and Periocular Biometric Pattern (FSVP-PBP) database. The results demonstrated the superiority of the algorithm over state-of-the-art methods, achieving remarkable classification accuracy of $99.3 \pm 0.02\%$ with the combined vein and periocular patterns.

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