综述深度学习在掌纹识别中的全链条应用与挑战
Deep Learning in Palmprint Recognition-A Comprehensive Survey
- 系统梳理深度学习在掌纹分割、特征提取等任务的进展
- 揭示当前方法在安全性与隐私保护方面的关键瓶颈
- 适合生物识别领域研究者快速掌握前沿方向
掌纹识别作为一项重要的生物特征技术，已广泛应用于多种场景。传统手工设计方法在表征能力上受限，过度依赖研究人员先验知识。深度学习（DL）因其在多个领域的显著成功，被引入以突破这一局限。尽管已有综述聚焦于掌纹识别中特定任务（多基于传统方法），但针对深度学习在掌纹识别各环节的全面研究仍存在明显空白。本文填补该空白，系统回顾了深度学习驱动下的最新进展，涵盖感兴趣区域分割、特征提取以及安全与隐私相关挑战。除总结现有成果外，本文还指出当前面临的核心挑战，并揭示未来研究的潜在机遇。通过整合最先进成果，本综述为研究人员提供重要参考，助其把握前沿技术并推动掌纹识别领域创新。
原文摘要 · Abstract (English)
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.
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