An effective recognition approach for contactless palmprint

The biometrics character has been widely used for individual identification and verification. Palmprint as one of biological features contains abundant discriminative features, which has already attracted a lot of interest. In this work, we focus on the identification and verification of contactless palmprint images. Considering the main differences between contact and contactless images, including orientation and deformation, we use a deep network combined with image alignment to further improve the recognition performance of contactless palmprint images. Recently, convolutional neural networks can well solve many classification problems, and researchers have proposed many networks with different architectures. We exploit the residual network in our framework, which achieves promising performance on the image classification problem. In order to improve the accuracy of verification, the spatial transformation network is used to align the image. The proposed method is tested on two public palmprint databases CASIA, GPDS. Extensive experiments are carried out with several state-of-the-art approaches as comparison, and the results demonstrated the effectiveness of our method.