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

arXiv: https://arxiv.org/abs/1812.11319 · HTML: https://mlsanity.com/paper/1812.11319/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [Child Palm-ID: Contactless Palmprint Recognition for Children](https://mlsanity.com/paper/2305.05161/) (2023)

## Abstract (arXiv)

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.