Efficient Deep Palmprint Recognition via Distilled Hashing Coding

Efficient deep palmprint recognition has become an urgent issue for the demand of personal identification on mobile/wearable devices. Compared to other biometrics, palmprint recognition has many unique advantages, e.g. richness of features, high user-friendliness, suitability for private security, etc. Existing deep learning based methods are computationally exhaustive in feature representation and learning, which are not suitable for large-scale deployment in portable authentication systems. In this paper, we combine hash coding and knowledge distillation to explore efficient deep palmprint recognition. Based on deep hashing network, palmprint images were converted to binary codes to save storage space and speed up matching. Combining hashing coding with knowledge distillation can further compress deep model to achieve an efficient recognition by light networks. Unlike previous palmprint recognition on datasets collected by dedicated devices in a controlled environment, we establish a novel database for unconstrained palmprint recognition, which consists of more than 30,000 images collected by 5 different mobile phones. Moreover, we manually labeled 14 key points on each image for region of interest (ROI) extraction. Comprehensive experiments were conducted on this palmprint database. The results indicate the feasibility of our database and the potential of palmprint recognition to be used as an efficient biometrics for deployment on consumer devices.