Deep Distillation Hashing for Unconstrained Palmprint Recognition

Deep palmprint recognition has become an emerging issue with great potential for personal authentication on handheld and wearable consumer devices. Previous studies of palmprint recognition are mainly based on constrained data sets collected by dedicated devices in controlled environments, which has to reduce the flexibility and convenience. In addition, general deep palmprint recognition algorithms are often too heavy to meet the real-time requirements of embedded system. In this article, a new palmprint benchmark is established, which consists of more than 20 000 images collected by five brands of smartphones in an unconstrained manner. Each image has been manually labeled with 14 key points for the region of interest (ROI) extraction. Furthermore, a novel deep distillation hashing (DDH) algorithm is proposed as a benchmark for efficient deep palmprint recognition. Palmprint images are converted to binary codes to improve the efficiency of feature matching. Derived from knowledge distillation, new distillation loss functions are constructed to compress the deep model to further improve the efficiency of feature extraction on the light network. Comprehensive experiments are conducted on both constrained and unconstrained palmprint databases. Using DDH, the accuracy of palmprint identification can be increased by up to 11.37%, and the equal error rate (EER) of palmprint verification can be reduced by up to 3.11%. The results indicate the potential of our database, and DDH can outperform other baselines to achieve the state-of-the-art performance. The collected data set is publicly available at http://gr.xjtu.edu.cn/web/bell/resource.