From Geometry to Deep Learning: An Overview of Finger Knuckle Biometrics Recognition Approaches

Abstract

Biometric identification technologies are crucial for enhancing security through reliable personal authentication methods. Among these modalities, finger knuckle biometrics stands out for its distinctive and consistent features, offering a valuable alternative to more commonly used biometric traits. Unlike fingerprints, which are easily captured from the surface of the skin, knuckle prints present a unique challenge. Knuckle prints are not as readily accessible from surface scans due to their position and the intricacy of their features, which require specialized techniques for accurate capture and recognition. The paper comprehensively reviews the evolution from traditional geometric methods to advanced deep learning techniques in finger knuckle recognition. Our review covers both unimodal and multimodal approaches, discussing various recognition strategies and their effectiveness. We also discussed the performance of knuckle biometric systems using metrics such as accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and Equal Error Rate (EER). The paper also highlights the importance of publicly available knuckle datasets, which are essential for developing and evaluating FKP biometric systems. These datasets enable researchers to benchmark and improve recognition algorithms. This review is aimed at researchers, practitioners, and academics interested in biometric technologies, offering insights into current advancements and future directions in finger knuckle biometrics.

Keywords

• Biometric authentication
• finger knuckle biometrics
• biometric recognition
• deep learning
• geometric analysis
• feature extraction