Deep Learning in Palmprint Recognition: A Comprehensive Survey
这篇综述系统回顾了深度学习在掌纹识别中的最新进展，涵盖感兴趣区域分割、特征提取及安全隐私等关键任务，并指出当前挑战与未来研究方向，适合相关领域研究者快速了解前沿技术。
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 article bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. This article systematically examines progress across key tasks, including region-of-interest (ROI) segmentation, feature extraction, and security and privacy-oriented challenges. Beyond highlighting these advancements, this article 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.