指节纹识别综述  

Finger-Knuckle-Print Recognition: A Preliminary Review

作　　者：陆劲挺[1,2] 贾伟[2] 叶慧[1] 赵洋[2] 闵海[2] 余烨[2] 胡戎翔[3] LU Jingting JIA Wei YE Hui ZHAO Yang MIN Hai YU Ye HU Rongxiang(Institute of Industry and Equipment Technology, Hefei University of Technology, Hefei 230009 School of Computer and Information, Hefei University of Technology, Hefei 23009 Institute of Nuclear Energy Safety Technology, Hefei Institutes of Physical Science, Chinese Academy of Sceinces, Hefei 230031)

机构地区：[1]合肥工业大学工业与装备技术研究院,合肥230009 [2]合肥工业大学计算机与信息学院,合肥230009 [3]中国科学院合肥物质科学研究院核能安全技术研究所,合肥230031

出　　处：《模式识别与人工智能》2017年第7期622-636,共15页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61673157;61402018;61305006;61305093;61370167;61175022)资助~~

摘　　要：相比其它生物特征,指节纹具有特征丰富,采集设备价格低,易于结合手形、手指静脉及掌纹组成性能鲁棒的多模态识别系统等优点.文中首先介绍指节纹的定义、数据采集、预处理方法等,之后详细介绍各种指节纹识别算法及多模态识别方案.根据特征提取及匹配方法的不同,将指节纹识别算法分为6类:基于结构的算法、基于子空间学习的算法、基于编码的算法、基于纹理特征的算法、基于相关滤波器的算法和基于局部特征描述子的算法.回顾和总结各种算法的特点,展望未来指节纹识别的发展方向.Compared with face, fingerprint, and iris based biometrics systems, finger-knuckle-print recognition based biometrics system has stable features, and it can be collected by low cost device and be easily combined with palmprint, finger vein, and hand shape recognition to form a robust system. In this paper, the definition, the data acquisition and the preprocessing of finger-knuckle-print recognition are firstly introduced. Then, the feature extraction and matching algorithms as well as multi-modal methods are reviewed. The effective finger-knuckle-print recognition algorithms are roughly divided into six categories ： texture-based algorithm, structure-based algorithm, subspace learning-based algorithm, correlation filter-based algorithm, local descriptor-based algorithm and orientation coding-based algorithm. Finally, the development tendency of finger-knuckle-print recognition is forecasted.

关 键 词：生物特征识别 指节纹识别 特征表示 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]