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可撤销手掌纹脉二维PalmHash码的共轭认证 被引量:3

Conjugate verification of cancelable palm-print-vein using 2D-PalmHash code
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摘要 由于认证性、实用性和防伪性方面的多重优势,迫切需要开发融合掌纹和掌脉的双模态认证技术。作为新型的可撤销掌纹认证编码,二维PalmHash码(2DPHC)克服了直接使用原始掌纹特征的安全隐患和隐私问题。本文将2DPHC框架推广至可撤销掌脉,通过融合掌纹和掌脉的2DPHC,实现可撤销手掌纹脉认证。为了不增加双模态认证中的计算复杂度和模板数据量,对掌纹和掌脉的模板生成方向进行优选。在每种模态中,分别生成4个方向的2DPHC,并统计匹配分数协方差。根据小协方差选择策略,分别从掌纹和掌脉中选择低相关性的2个2DPHC作为可撤销共轭模板。通过均值策略融合多个模板的匹配分数,实现可撤销共轭认证。与认证性能排序选择策略相比,小协方差策略指导的方向选择可以更有效降低融合分数的方差,获得更高的共轭认证性能。 It is imperative to develop bimodal verification fusing palmprint and palmvein,which has high verification accuracy,good practicability and strong anti-spoof ability. 2D-PalmHash code (2DPHC), as a novel cancelable palmprint verification coding scheme, overcomes the secure vulnerahilities and privacy problems of direct usage of original palmprint features. The framework of 2DPHC can be generalized to cancelable palmvein. Cancelable palm-print-vein verification can be achieved by fusing 2DPHCs of palmprint and palmvein. In order to prevent the increase of computational cost and template data size in bimodal verification, it is 'necessary to effectively select the orientations, along which the palmprint and palmvein templates are generated. In each model biometric,2DPHCs are generated along four orientations, and the covariances hetween the matching scores of the four 2DPHCs are calculated. According to "small covariance" selection rule,two 2DPHCs with minimum covariance are respectively selected from palmprint and palmvein as cancelable conjugate templates. The matching scores of the templates are fused with mean rule for cancelable conjugate verification. Compared with the rule of "verification accuracy sequencing" , the "small covariance" rule for orientation selection can suppress the variance of the fused matching score more effectively,and accordingly achieve higher conjugate verification accuracy.
出处 《光电子.激光》 EI CAS CSCD 北大核心 2014年第9期1791-1795,共5页 Journal of Optoelectronics·Laser
基金 国家自然科学基金(61305010 61262019 61202112 61303199) 中国博士后科学基金(2013M531554) 南昌航空大学博士启动基金(EA201308058)资助项目
关键词 共轭认证 手掌纹脉 二维PalmHash码(2DPHC) 可撤销生物特征 方向选择 小协方差准则 conjugate verification palm-print-vein 2D PalmHash code (2D-PHC) cancelable biometric orientation selection small covariance rule
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  • 1李文新,夏胜雄,张大鹏,许卓群.基于主线特征的双向匹配的掌纹识别新方法[J].计算机研究与发展,2004,41(6):996-1002. 被引量:34
  • 2苏广志,谢遵江,高江涛,贺业春.手部静脉的应用解剖学研究[J].哈尔滨医科大学学报,2005,39(5):425-426. 被引量:4
  • 3YAO Yong-fang, JING Xiao-yuan, WONG Han-san. Face and palmprint feature level fusion for single sample bio- metrics recognition[J]. Neurocomputing, 2007,70 ( 7-9 ) : 1582-1586.
  • 4SHEN Lin-lin, BAI Li, JI Zhen. Hand-based biometrics fu- sing palmprint and finger-knuckle-print[A]. International workshop on emerging techniques and challenges for hand-based biometrics[C]. 2010,1-4.
  • 5ZHANG Yan-qiang, SUN Dong-mei, QIU Zheng-ding. Hand- based feature level fusion for single sample biometrics recognition[A]. International workshop On emerging tech- niques and challenges for hand-based biometrics[C]. 2010,1-4.
  • 6ZHANG Yan-qiang, SUN Dong-mei, QIU Zheng-ding. Hand- based single sample biometrics recognition[A]. Neural Computing & Applications[C]. 2011,1-10.
  • 7GUO Jin-yu, LIU Yu-qin, YUAN Wei-qi. Palmprint recogni-tion using local information from a single image per per- son[J]. Journal of Computational Information Systems, 2012,8(8) :3199-3206.
  • 8Fujiwara Koichi, Kano Manabu, Hasebe Shinji. Develop- ment of correlation-based pattern recognition algorithm and adaptive soft-sensor design[J]. Control Engineering Practice,2012,20(4) :371-378.
  • 9Fujiwara Koichi, Kano Manabu, Hasebe Shinji. Soft-sensor development using correlation-based just-in-time model- ing[J]. AIChE Journal, 2009,55 (7) : 1754-1765.
  • 10Kohir V V,Desai U B. Face recognition using a DCT-HMM approach[A]. Proc. of the Fourth IEEE Workshop on Ap- plications of Computer Vision[C]. ]998,226-231.

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  • 1李杰,郝晓莉.一种基于椭圆肤色模型的人脸检测方法[J].计算机测量与控制,2006,14(2):170-171. 被引量:12
  • 2WU Xiang-qian, ZHAO Qiu-shi, BU Wei. A SIFT-based contactless palmprint verification approach using itera- tive RANSAC and local palmprint descriptors[J]. Pattern Recognition, 2014,47 (10) :3314-3326.
  • 3GUO Jin-yu,YUAN Wei-qi. Palmprint recognition based on kernel principal component analysis and Fisher linear dis- criminant[J]. Journal of Optoelectrorics Laser, 2008,19(12):1698-1701.
  • 4L I Ya-jing, TAN Zhi-ming, ZHAN Yong-qiang. Two-dimen- sional bilinear preserving projections for image feature extraction and classification [J]. Neural Computing and Applications,2014,24(3-4) : 901-909.
  • 5GUI Jie, JIA Wei, ZHU Ling, et al. Locality preserving dis- criminant projections for face and palmprint recognition [J]. Neurocomputing,2010,73(13-15) : 2696-2707.
  • 6GUO Jin-yu,OHEN Hai-bin, LI Yuan. Palmprint recognition based on multilinear principal component analysis and Fisher linear discriminant[J]. Journal of Computational In- formation Systems,2013,9(17) : 6859 -6866.
  • 7GUO Jin-yu,CHEN Hai-bin, LI Yuan. Palmprint recognition based on local Fisher discriminant analysis[J]. Journal ofSoftware,2014,9(2) :287-292.
  • 8ZHENG Zhong-long, YANG Fan, TAN We-nan, et al. Gabor feature-based face recognition using supervised locality preserving projection [J]. Signal Processing, 2007, 87 (10) : 2473-2483.
  • 9Lafon S,Keller Y,Coifman R R. Data fusion and multicue data matching by diffusion maps[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006,2,8 ( 11 ) : 1784-1797.
  • 10HE Xiao-feng, 'fAN Shui-cheng, HU Yu-xiao, et al. Face recognition using Laplacianfaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27 (3) :328-340.

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