A Deep Learning based Framework to Detect and Recognize Humans using Contactless Palmprints in the Wild
This work addresses the problem of secure and convenient human identification for applications like biometric security, offering a more generalizable solution than prior methods.
The paper tackles contactless palmprint recognition in unconstrained environments by proposing a deep learning framework that uses a fully convolutional network with residual features and a soft-shifted triplet loss, achieving superior performance and generalization without database-specific tuning compared to existing methods.
Contactless and online palmprint identfication offers improved user convenience, hygiene, user-security and is highly desirable in a range of applications. This technical report details an accurate and generalizable deep learning-based framework to detect and recognize humans using contactless palmprint images in the wild. 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 report 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.