Palm print recognition system using siamese network and transfer learning

Abstract

This paper proposes a palmprint authentication approach using a one-shot learning technique based on similarity instead of classification (used by most other proposals). The one-shot learning technique uses the siamese network architecture built on top of the pre-trained VGG16 to efficiently reduce the cost and time of training the siamese network. This technique allows the user registration using only one palmprint and then performs the authentication process by performing a siamese similarity measure instead of classification techniques. The proposed model achieved high accuracies scores of 97%, 96.7% for Tongji datasets, 92.3%, 91.9% for PolyU-IITD datasets, 90.9%, 88.3% for CASIA datasets and 95.5% for COEP dataset. These performances were measured based on the testing dataset for unseen persons while the siamese training dataset was applied to different persons. The proposed model uses the pre-trained part of VGG16 as a feature extraction part then feeds the generated feature vector into the Euclidean distance layer that is trained in conjunction with the sigmoid layer to output the final similarity decision. Compared to other models, this proposed model achieved a high average accuracy of 93.2% and 0.19 EER over the available four palm print datasets which is generalized over proposals. All codes are open-source and available online at https://github.com/ProjectsRebository/PalmPrint-recognition-using-Transfer-Learning.