Deep Learning in Palmprint Recognition-A Comprehensive Survey

2025-01-02

AI Summary

This paper addresses core challenges in palmprint recognition—low texture discriminability, high intra-class deformation, poor cross-domain generalization, and inadequate privacy preservation. To tackle these, it systematically surveys deep learning (DL) advancements across the entire pipeline: region-of-interest (ROI) segmentation, robust feature extraction, and secure learning. It introduces the first DL-driven analytical framework specifically tailored for palmprint recognition, integrating CNNs, Transformers, self-supervised learning, GANs, and federated learning to align with palmprint-specific characteristics. The survey synthesizes over 100 recent works—filling critical gaps left by prior reviews—and identifies key bottlenecks. It proposes three concrete research directions: multimodal fusion, lightweight modeling, and privacy-enhancing learning. The work delivers a reproducible methodology guide and a curated list of open problems, thereby advancing palmprint recognition toward practicality, robustness, and trustworthiness.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Deep Neural Architectures and Foundation Models
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Deep Learning
• Palmprint Recognition
• Privacy Protection

Innovation

• Deep Learning
• Palm Recognition
• Privacy Protection