# Improving Hand Recognition in Uncontrolled and Uncooperative Environments using Multiple Spatial Transformers and Loss Functions

arXiv: https://arxiv.org/abs/2311.05383 · HTML: https://mlsanity.com/paper/2311.05383/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 72 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Learning a Fixed-Length Fingerprint Representation](https://mlsanity.com/paper/1909.09901/) (2019)
- [11K Hands: Gender recognition and biometric identification using a large dataset of hand images](https://mlsanity.com/paper/1711.04322/) (2017)
- [Automated Latent Fingerprint Recognition](https://mlsanity.com/paper/1704.01925/) (2017)
- [Palmprint Recognition in Uncontrolled and Uncooperative Environment](https://mlsanity.com/paper/1911.12514/) (2019)
- [From Noise to Feature: Exploiting Intensity Distribution as a Novel Soft Biometric Trait for Finger Vein Recognition](https://mlsanity.com/paper/2112.07931/) (2021)
- [Local Multiple Directional Pattern of Palmprint Image](https://mlsanity.com/paper/1607.06166/) (2016)

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

- [Palmprint De-Identification Using Diffusion Model for High-Quality and Diverse Synthesis](https://mlsanity.com/paper/2504.08272/) (2025)

## Abstract (arXiv)

The prevalence of smartphone and consumer camera has led to more evidence in the form of digital images, which are mostly taken in uncontrolled and uncooperative environments. In these images, criminals likely hide or cover their faces while their hands are observable in some cases, creating a challenging use case for forensic investigation. Many existing hand-based recognition methods perform well for hand images collected in controlled environments with user cooperation. However, their performance deteriorates significantly in uncontrolled and uncooperative environments. A recent work has exposed the potential of hand recognition in these environments. However, only the palmar regions were considered, and the recognition performance is still far from satisfactory. To improve the recognition accuracy, an algorithm integrating a multi-spatial transformer network (MSTN) and multiple loss functions is proposed to fully utilize information in full hand images. MSTN is firstly employed to localize the palms and fingers and estimate the alignment parameters. Then, the aligned images are further fed into pretrained convolutional neural networks, where features are extracted. Finally, a training scheme with multiple loss functions is used to train the network end-to-end. To demonstrate the effectiveness of the proposed algorithm, the trained model is evaluated on NTU-PI-v1 database and six benchmark databases from different domains. Experimental results show that the proposed algorithm performs significantly better than the existing methods in these uncontrolled and uncooperative environments and has good generalization capabilities to samples from different domains.