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

TL;DR

This paper introduces a novel hand recognition method using multiple spatial transformers and loss functions to improve accuracy in uncontrolled, uncooperative environments, demonstrating superior performance across various datasets.

Contribution

The paper proposes an integrated algorithm combining multi-spatial transformer networks and multiple loss functions for enhanced hand recognition in challenging conditions.

Findings

Significantly outperforms existing methods in uncontrolled environments

Achieves high accuracy across multiple benchmark datasets

Demonstrates strong generalization to different domains

Abstract

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.