Unconstrained Hand Recognition Using Thermal Infrared Sensing of Dorsal Veins

Dorsal hand vein recognition, and vascular bio-metrics in general, is becoming an increasingly reliable and trusted way to recognize people. However, previous technology limits vascular biometrics to cooperative individuals - requiring a person to hold their hand still next to a near infrared sensor [16]. In this paper, we show that we can extend the range and utility of this biometric using long wave infrared (LWIR) imaging. LWIR shows the thermal gradient on the dorsal side of the hand revealing the patterns related to the underlying vascular structure. To study this problem, we introduce Thermal CoAct, a database of people performing common actions while their hands are sensed in the LWIR region using two different sensing technologies. To identify a person, we fine-tune a Vision Transformer (ViT). Our experimental results show that we can successfully recognize unconstrained hands in both open-world and closed-world scenarios. We explore how different sensing technologies as well as different styles of enrollment affect performance. When paired with a good enrollment strategy and sensor, we have achieved a rank-1 accuracy of 96.7% for closed-world scenarios and equal error rates (EER) of 3.1 % for open-world scenarios.