Artabaz Saliha, Sliman Layth
Laboratoire de Méthodes de Conception de Systèmes LMCS, Ecole nationale Supérieure d'Informatique ESI, Oued-Smar, Algiers, Algeria.
Efrei Research Lab, Paris Panthéon Assas University, Villejuif, France.
Sci Rep. 2025 Aug 10;15(1):29237. doi: 10.1038/s41598-025-10075-1.
Feature fusion is a widely adopted strategy in multi-biometrics to enhance reliability, performance and real-world applicability. While combining multiple biometric sources can improve recognition accuracy, practical performance depends heavily on feature dependencies, redundancies, and selection methods. This study provides a comprehensive analysis of multimodal hand biometric recognition systems. We aim to guide the design of efficient, high-accuracy biometric systems by evaluating trade-offs between classical and learning-based approaches. For feature extraction, we employ Zernike moments and log-Gabor filters, evaluating multiple selection techniques to optimize performance. While baseline palmprint and fingerprint systems exhibit varying classification rates. Our feature fusion method achieves a consistent 99.29% identification rate across diverse classifiers. Additionally, we explore EfficientNET as an end-to-end feature extractor and classifier, comparing its fusion performance with the traditional approach. Our findings emphasize feature selection as the key of building efficient and stable recognition systems. Using the minimal optimal feature set, we achieve an equal error rate (EER) of 0.71%, demonstrating superior efficiency and accuracy.
特征融合是多生物特征识别中广泛采用的一种策略，用于提高可靠性、性能和实际适用性。虽然组合多个生物特征源可以提高识别准确率，但实际性能在很大程度上取决于特征依赖性、冗余性和选择方法。本研究对多模态手部生物特征识别系统进行了全面分析。我们旨在通过评估经典方法和基于学习的方法之间的权衡，来指导高效、高精度生物特征系统的设计。对于特征提取，我们采用泽尼克矩和对数伽柏滤波器，评估多种选择技术以优化性能。虽然基线掌纹和指纹系统表现出不同的分类率。我们的特征融合方法在不同分类器上实现了一致的99.29%的识别率。此外，我们探索将EfficientNET作为端到端特征提取器和分类器，并将其融合性能与传统方法进行比较。我们的研究结果强调特征选择是构建高效稳定识别系统的关键。使用最小最优特征集，我们实现了0.71%的等错误率（EER），证明了卓越的效率和准确性。
