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Contactless and online palmprint identfication offers improved userconvenience, hygiene, user-security and is highly desirable in a range ofapplications. This technical report details an accurate and generalizable deeplearning-based framework to detect and recognize humans using contactlesspalmprint images in the wild. Our network is based on fully convolutionalnetwork that generates deeply learned residual features. We design asoft-shifted triplet loss function to more effectively learn discriminativepalmprint features. Online palmprint identification also requires a contactlesspalm detector, which is adapted and trained from faster-R-CNN architecture, todetect palmprint region under varying backgrounds. Our reproducibleexperimental results on publicly available contactless palmprint databasessuggest that the proposed framework consistently outperforms several classicaland state-of-the-art palmprint recognition methods. More importantly, the modelpresented in this report offers superior generalization capability, unlikeother popular methods in the literature, as it does not essentially requiredatabase-specific parameter tuning, which is another key advantage over othermethods in the literature.

A Deep Learning based Framework to Detect and Recognize Humans using Contactless Palmprints in the Wild