Bridging the Divide Between Left and Right Palmprints for Cross-Chirality Verification

Palmprint recognition has emerged as a prominent biometric authentication method due to its high-discriminative power, making it suitable for IoT-based security applications. However, the traditional verification paradigm—requiring identical query and registered palmprints—poses notable limitations. This approach is inconvenient if the registered palmprint is injured. To address these challenges, we draw inspiration from biological insights into the symmetrical development of structures during embryonic growth and propose a novel cross-chirality (CC) Palmprint verification (CCPV) framework. CCPV enables authentication using either palm, irrespective of which palm is registered, enhancing flexibility for IoT deployments with diverse user conditions. CCPV incorporates an innovative matching rule to improve robustness and minimize variability. This rule calculates distances by flipping the gallery and query palmprints, averaging the results to produce the final matching score. Considering all potential alignments, this approach reduces variance and boosts reliability, which is critical for ensuring seamless biometric authentication in IoT systems. Complementing this is the CC loss, which fosters a robust feature space tailored to CC matching. The CC loss ensures consistency across four palmprint variants—left, right, flipped left, and flipped right—enabling the model to extract chirality-consistent features. Extensive experiments on public datasets validate our effectiveness under closed-set and open-set scenarios. Furthermore, we demonstrate that CCPV is versatile and can seamlessly integrate with existing palmprint recognition methods to achieve superior performance. This innovation advances state-of-the-art biometric authentication and paves the way for more resilient palmprint recognition systems for IoT applications.