Deep Learning in Palmprint Recognition-A Comprehensive Survey

TL;DR

This survey comprehensively reviews recent deep learning advancements in palmprint recognition, covering key tasks, challenges, and future opportunities to guide researchers in the field.

Contribution

It provides a thorough overview of DL-based palmprint recognition methods across all key tasks, filling a gap left by previous narrow surveys.

Findings

Deep learning improves palmprint recognition accuracy.

Identification of current challenges in DL-based methods.

Highlighting promising future research directions.

Abstract

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition.