Deep Distillation Hashing for Palmprint and Finger Vein Retrieval

Abstract

With the increasing application of biometric recognition technology in daily life, the number of registered users is rapidly growing, making fast retrieval techniques increasingly important for biometric recognition. However, existing biometric recognition models are often overly complex, making them difficult to deploy on resource-constrained terminal devices. Inspired by knowledge distillation (KD) for model simplification and deep hashing for fast image retrieval, we propose a new model that achieves lightweight palmprint and finger vein retrieval. This model integrates hash distillation loss, classification distillation loss, and supervised loss from labels within a KD framework. And it improves the retrieval and recognition performance of the lightweight model through the network design. Experimental results demonstrate that this method promotes the performance of the student model on multiple palmprint and finger vein datasets, with retrieval precision and recognition accuracy surpassing several existing advanced hashing methods.