Automated Person Identification from Hand Images using Hierarchical Vision Transformer Network

Authors

Zahra Ebrahimian, Seyed Ali Mirsharji, Ramin Toosi, Mohammad Ali Akhaee

Abstract

Nowadays, person identification is widely used for security purposes. Identity verification is done using a variety of techniques. Biometric authentication is the most well-known and popular secure kind of authentication in most devices. In this research, dorsal and palmar hand images, which are regarded as two important biometric characteristics, are both used for biometric authentication. In order to take into account both global and local variables for determining human identity, We propose a two-stream hierarchical vision transformer with two independent inputs of the whole hand image and knuckle sub-images drawn from the 11k-Hand dataset. As a result of this approach, we achieved an accuracy of 99.4% and an error rate of 2.47% to identify people.