掌纹识别研究进展综述

掌纹图像蕴含丰富特征,容易与手背静脉、指节纹及手形特征进行多模态融合,因此成为生物特征识别领域的热点.文中主要从掌纹的采集、感兴趣区域的检测、特征提取与匹配3方面介绍掌纹识别的基本流程.探讨基于不同特征融合的多模态识别策略.根据特征提取方法的不同,掌纹识别算法可分为基于手工设计的算法(如编码特征、结构特征、统计特征、子空间特征)和基于特征学习的算法(如机器学习和深度学习),文中对上述算法进行详细对比和分析.最后讨论未来掌纹识别面临的挑战和发展,特别是复杂场景下跨平台的掌纹识别系统.

Progress of Palmprint Recognition: A Review

Palmprint images contain rich features and they can be easily combined with hand dorsal veins, finger-knuckle-prints and hand shapes to form multimodal features. Thus, palmprint recognition becomes a hot topic in the field of biometric recognition. In this paper, the basic process of palmprint recognition is discussed from three aspects: the collection of palmprint images, region of interest detection, and feature extraction and matching. Multimodal methods based on fusion of different features are also explored. Besides, on the basis of feature extraction means, palmprint recognition algorithms are roughly divided into hand-craft based algorithms including encoding, structure and statistics, and subspace and feature learning based algorithms including machine learning and deep learning. The algorithms are compared and analyzed in detail. Finally, challenges and future perspectives in palmprint recognition are discussed, especially palmprint recognition system in complex environment across different devices.