Abstract
With the growing demand for hand hygiene, touchless palmprint recognition has made great strides recently, providing an effective solution for personal identification. Despite many efforts devoted to this area, the discriminative ability of touchless palmprint is still uncertain, especially for large-scale datasets. To address this problem, in this study, a large-scale touchless palmprint dataset was built containing 2334 palms from 1167 individuals. To the best of our knowledge, this is currently the largest touchless palmprint image benchmark regarding the number of individuals and palms. In addition, a novel deep learning framework was proposed for touchless palmprint recognition, referred to as 3D convolution palmprint recognition network (3DCPN), which leverages 3D convolutions to dynamically integrate multiple Gabor features. In 3DCPN, a novel variant of the Gabor filter is embedded into the first layer to enhance curve feature extraction. Using a well-designed ensemble scheme, low-level 3D features are convolved to extract high-level features. Finally, a region-based loss function is implemented to strengthen the discriminative ability of both global and local descriptors. Extensive experiments were conducted on the newly built dataset and other popular databases to demonstrate the superiority of our method. The results show that the proposed 3DCPN achieves state-of-the-art or comparable performance.