Contactless Palmprint Identification Using Deeply Learned Residual Features
Yang Liu, Ajay Kumar
Abstract
Contactless and online palmprint identification offers improved user convenience, hygiene, user-security and is highly desirable in a range of applications. This paper proposes an accurate and generalizable deep learning-based framework for the contactless palmprint identification. Our network is based on fully convolutional network that generates deeply learned residual features. We design a soft-shifted triplet loss function to more effectively learn discriminative palmprint features. Online palmprint identification also requires a contactless palm detector, which is adapted and trained from faster-R-CNN architecture, to detect palmprint region under varying backgrounds. Our reproducible experimental results on publicly available contactless palmprint databases suggest that the proposed framework consistently outperforms several classical and state-of-the-art palmprint recognition methods. More importantly, the model presented in this paper offers superior generalization capability, unlike other popular methods in the literature, as it does not essentially require database-specific parameter tuning, which is another key advantage over other methods in the literature.