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.

翻译：掌纹识别已成为一种重要的生物识别技术，广泛应用于多种场景。传统的掌纹识别手工方法在表示能力上往往存在不足，因为它们严重依赖于研究者的先验知识。深度学习（DL）已被引入以解决这一局限性，并凭借其在各领域取得的显著成功而受到关注。尽管现有综述通常局限于掌纹识别中的特定任务——且多基于传统方法——但在全面探索基于深度学习的掌纹识别各方面方法的研究上仍存在显著空白。本文通过深入回顾基于深度学习的掌纹识别的最新进展，填补了这一空白。本文系统性地审视了关键任务上的进展，包括感兴趣区域分割、特征提取以及面向安全/隐私的挑战。除了突出这些进展外，本文还指出了当前面临的挑战，并揭示了未来研究的有望机遇。通过整合最先进的进展，本综述为研究人员提供了宝贵的资源，帮助他们紧跟前沿技术，并推动掌纹识别领域的创新。