Cross-Chirality Palmprint Verification: Left is Right for the Right Palmprint

• URL: http://arxiv.org/abs/2409.13056v1
• Date: Thu, 19 Sep 2024 19:10:21 GMT
• Title: Cross-Chirality Palmprint Verification: Left is Right for the Right Palmprint
• Authors: Chengrui Gao, Ziyuan Yang, Tiong-Sik Ng, Min Zhu, Andrew Beng Jin Teoh,
• Abstract summary: This paper introduces a novel Cross-Chirality Palmprint Verification (CCPV) framework that challenges the conventional wisdom in traditional palmprint verification systems. Unlike existing methods that typically require storing both left and right palmprints, our approach enables verification using either palm while storing only one palmprint template.
• Score: 11.388567430575783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Palmprint recognition has emerged as a prominent biometric authentication method, owing to its high discriminative power and user-friendly nature. This paper introduces a novel Cross-Chirality Palmprint Verification (CCPV) framework that challenges the conventional wisdom in traditional palmprint verification systems. Unlike existing methods that typically require storing both left and right palmprints, our approach enables verification using either palm while storing only one palmprint template. The core of our CCPV framework lies in a carefully designed matching rule. This rule involves flipping both the gallery and query palmprints and calculating the average distance between each pair as the final matching distance. This approach effectively reduces matching variance and enhances overall system robustness. We introduce a novel cross-chirality loss function to construct a discriminative and robust cross-chirality feature space. This loss enforces representation consistency across four palmprint variants: left, right, flipped left, and flipped right. The resulting compact feature space, coupled with the model's enhanced discriminative representation capability, ensures robust performance across various scenarios. We conducted extensive experiments to validate the efficacy of our proposed method. The evaluation encompassed multiple public datasets and considered both closed-set and open-set settings. The results demonstrate the CCPV framework's effectiveness and highlight its potential for real-world applications in palmprint authentication systems.

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