Finger vein identification system using capsule networks with hyperparameter tuning

Abstract

Safety and security systems are essential for personnel who need to be protected and valuables. The security and safety system can be supported using a biometric system to identify and verify permitted users or owners. Finger vein is one type of biometric system that has high-level security. The finger vein biometrics system has two primary functions: identification and verification. Safety and security technology development is often followed by hackers' development of science and technology. Therefore, the science and technology of safety and security need to be continuously developed. The paper proposes finger vein identification using capsule networks with hyperparameter tuning. The augmentation, convolution layer parameters, and capsule layers are optimized. The experimental results show that the capsule network with hyperparameter tuning successfully identifies the finger vein images. The system achieves an accuracy of 91.25% using the Shandong University machine learning and applications-homologous multimodal traits (SDUMLA-HMT) dataset.