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.
Highlights
Many biometric systems are based on biological features, such as iris (Liu et al, 2017), retina, face (Moridani et al, 2020), voice, character, fingerprint (Rivaldería et al, 2017), and DNA, etc. have been successfully developed for many commercial applications due to rapid growth in hardware technology in terms of computing speed and highresolution capture devices (Kumar and Srinivasan, 2012; Liu et al, 2017)
Pre-trained models are used in transfer learning as the starting point for computer vision and natural language processing tasks, given the vast computational and time resources needed to develop neural network models on these issues and the enormous skill jumps they provide on related issues
The inductive transfer is known as this method of transfer learning used in deep learning
Summary
Many biometric systems are based on biological features, such as iris (Liu et al, 2017), retina, face (Moridani et al, 2020), voice, character, fingerprint (Rivaldería et al, 2017), and DNA, etc. Over the last 4-5 years, fingerprint, face, and iris technologies have been used in laptops as a safety feature. There are still few constraints on biometric systems today, limiting their use and accuracy in civilian and forensic applications. A Palm Print meets the basic requirements (Dian and Dongmei, 2016) for personal authentication as a universal, special and permanent biometric pattern because the palm line features; such as palm lines, creases, ridges, minutiae, and delta points, are stable and remain unchanged throughout the life of an individual (Zhang et al, 2012; Kong et al, 2009; Zhang et al, 2017)
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