Palmprint Recognition Using Realistic Animation Aided Data Augmentation

In this paper, a palmprint augmentation algorithm based on 3D animation is proposed for enhancing contactless palmprint recognition performance. Contactless palmprint varies in position, orientation and musculoskeletal deformations. As the existing contactless databases are small, they contain only a few such variations of a palm. Popular data augmentation approaches, including translation, rotation and scaling, have been used to increase the dataset size and its diversity, but these methods do not simulate non-linear deformation of the hand. Some researchers have used 3D and computer graphic techniques to generate more data for training deep networks. These techniques are application-specific. The proposed algorithm makes use of a 3D hand model to simulate muscular and skeletal deformations of the hand. The deformations from the 3D model are applied to 2D palmprint images to generate new palmprint images with the same identities. Four deep networks, Alexnet, VGG-16, Resnet-50 and Inception-V3 and two contactless palmprint databases, IITD and CASIA, are employed to evaluate the proposed algorithm. The proposed algorithm is compared with the standard augmentation methods. The experimental results show that the proposed augmentation algorithm reduces EER and Rank-1 error rate.