# Palmprint Recognition in Uncontrolled and Uncooperative Environment

arXiv: https://arxiv.org/abs/1911.12514 · HTML: https://mlsanity.com/paper/1911.12514/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification](https://mlsanity.com/paper/2308.00451/) (2023)
- [Mobile Contactless Palmprint Recognition: Use of Multiscale, Multimodel Embeddings](https://mlsanity.com/paper/2401.08111/) (2024)
- [Deep Learning in Palmprint Recognition-A Comprehensive Survey](https://mlsanity.com/paper/2501.01166/) (2025)
- [EgoPrivacy: What Your First-Person Camera Says About You?](https://mlsanity.com/paper/2506.12258/) (2025)
- [Re-identification from histopathology images](https://mlsanity.com/paper/2403.12816/) (2024)
- [Diff-Palm: Realistic Palmprint Generation with Polynomial Creases and Intra-Class Variation Controllable Diffusion Models](https://mlsanity.com/paper/2503.18312/) (2025)
- [BPFNet: A Unified Framework for Bimodal Palmprint Alignment and Fusion](https://mlsanity.com/paper/2110.01179/) (2021)
- [GenPalm: Contactless Palmprint Generation with Diffusion Models](https://mlsanity.com/paper/2406.00287/) (2024)
- [Touchless Palmprint Recognition based on 3D Gabor Template and Block Feature Refinement](https://mlsanity.com/paper/2103.02167/) (2021)
- [Advancing 3D finger knuckle recognition via deep feature learning](https://mlsanity.com/paper/2301.02934/) (2023)
- [Improving Hand Recognition in Uncontrolled and Uncooperative Environments using Multiple Spatial Transformers and Loss Functions](https://mlsanity.com/paper/2311.05383/) (2023)
- [X-Palm: Paired Multispectral-to-Smartphone Dataset for Cross-Domain Palmprint Authentication](https://mlsanity.com/paper/2606.08437/) (2026)
- [Geometric Synthesis: A Free lunch for Large-scale Palmprint Recognition Model Pretraining](https://mlsanity.com/paper/2203.05703/) (2022)
- [Palmprint De-Identification Using Diffusion Model for High-Quality and Diverse Synthesis](https://mlsanity.com/paper/2504.08272/) (2025)

## Abstract (arXiv)

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods.