Palmprint Recognition in Uncontrolled and Uncooperative Environment

TL;DR

This paper introduces a new deep learning-based palmprint recognition method and a novel database for palmprint images collected in uncontrolled environments, improving identification accuracy in forensic scenarios.

Contribution

The study presents a new large-scale palmprint database from the Internet and an end-to-end deep learning algorithm tailored for uncontrolled environment palmprint recognition.

Findings

Proposed algorithm outperforms state-of-the-art methods.

New database NTU-PI-v1 contains 7881 images from 2035 palms.

Effective recognition in uncontrolled, uncooperative environments.

Abstract

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.