A deep biometric hash learning framework for three advanced hand‐based biometrics

Abstract

Abstract Hand‐based biometrics has undergone extensive research in recent decades. Besides fingerprint, which is commonly used in personal authentication, there are other three advanced hand‐based biometrics that are further researched worldwide, such as palmprint, palm vein, and dorsal hand vein. However, the academics mainly focus on their unimodal or multimodal recognitions, and few researchers conduct comprehensive comparisons for them to guide practical applications, that is, to assess which one is the suitable biometric modality. Inspired by deep hashing network (DHN) and transfer learning, we propose a deep biometric hash learning (DBHL) framework to uniformly analyse and deal with these three biometrics. An end‐to‐end network is involved to convert images into binary codes. Pre‐trained network is employed for fine‐tuning, and the hamming distance is adopted to measure the similarity between the codes of query and registration images. Through experiments on benchmarks, equal error rate (EER) and the maximum or minimum distance of genuines or imposters are obtained as performance metrics. In experiments, EERs of palmprint and palm vein recognitions reach 0%, and EER of dorsal hand vein recognition is as low as 0.196%. It shows that the DBHL framework is effective to construct hand‐based biometric systems. Furthermore, a plain comparison among them shows that palmprint is more suitable in practical applications.

Keywords

• binary codes
• biometrics (access control)
• feature extraction
• fingerprint identification
• image recognition
• learning (artificial intelligence)