Deep Learning in Palmprint Recognition-A Comprehensive Survey

TL;DR

This survey maps the rise of deep learning in palmprint recognition, detailing progress from ROI preprocessing to advanced feature extraction and security/privacy considerations. It synthesizes DL-based closed-set and open-set approaches, cross-domain and cross-modality techniques, and lightweight designs, while highlighting dataset and evaluation gaps that hinder generalization. The paper also surveys attacks on palmprint systems and the corresponding countermeasures, including privacy-preserving methods, cancelable templates, and anti-spoofing strategies, and discusses future opportunities such as synthetic data, domain adaptation, and potential integration with LLMs. Overall, it provides a comprehensive foundation for researchers to understand current capabilities, limitations, and directions for robust, scalable palmprint recognition in practical deployments.

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.

Figures (4)

• Figure 1: The pipeline of the DL-based palmprint recognition system.
• Figure 2: Overall structure of this paper.
• Figure 3: The evolution of palmprint feature extraction methodologies.
• Figure 4: The illustration of closed-set and open-set palmprint recognition scenario.