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量化层多生物特征融合的最佳权值 被引量：3

Optimal weight of the score level multiple biometric feature fusion
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摘要 该文对多生物特征融合中的量化层融合进行了研究,给出了能使识别系统性能提高的最佳权值方案。论文首先提出了一种便于对融合策略进行研究的量化值归一化模型。然后针对3个与生物特征识别性能有关的参数,错误接受率FAR、错误拒绝率FRR和等错率EER,分别进行讨论,推导出使它们降到最小时的权值条件。最后以掌纹特征为例,进行多生物特征融合实验。结果表明了量化层融合能有效提高系统识别性能。 This paper describes an optimal weighting strategy for improving the performance of recognition systems by emphasizing the score level fusion of multimodal biometrics. The system uses a score normalization model which predigests the fusion strategy research. Then, a weighted combination of three parameters （false acceptance rate （FAR）, false rejection rate （FRR）, equal error rate （EER）） is minimized to give the best recognition result. Tests using palm prints as an example, show the effectiveness of score level fusion for promoting the performance of recognition systems.
作者 周斌 林喜荣 贾惠波 周永冠
机构地区 清华大学精密仪器与机械学系 清华大学深圳研究生院
出处 《清华大学学报（自然科学版）》 EI CAS CSCD 北大核心 2008年第2期192-195,共4页 Journal of Tsinghua University(Science and Technology)
关键词 信息处理 生物特征 多特征融合 量化层融合 量化值归一化 information process biometric multiple feature fusion score fusion score normalization
分类号 TP391.4 [自动化与计算机技术—计算机应用技术] O235 [理学—运筹学与控制论]
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参考文献9

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同被引文献23

  • 1LIU Xiang, SU Qian-min, DENG Zi-bing. The Implementation of Analysis System of Vehicle Impact Based on Dynamic Image Sequence [ C ] // Proceedings of the 2008 International Conference on Computer Science and Software Engineering. Washington, DC, USA: IEEE Computer Society, 2008: 305-308.
  • 2HONG L, JAIN A K, PANKANTI S. Can Muhibiometrics Improve Performance [ C] //J Proc AutoID99. New York: IEEE, 2000 : 59-64.
  • 3YIN Zhao-zheng, PORIKLI F, COLLINS R T. Likelihood Map Fusion for Visual Object Tracking [ C]// Proceedings of IEEE 2008 Workshop on Application of Computer Vision. Piscataway, NJ : IEEE Computer Society, 2008 : 1-7.
  • 4ROBERT SNELICK, UMUT ULUDAG, ALAN MINK, et al. Large Scale Evaluation of Multimodal Biometric Authentication Using State-of-the-Art Systems [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27 (3) : 450- 455.
  • 5MEIER T, NGUN K N. Video Segmentation for Content-Based Coding [ J ]. IEEE Trans on Circuits and Systems for Video Technology, 1999, 9 (8): 1190-1203.
  • 6EVANSA N, LIU X U. A Morphological Gradient Approach to Color Edge Detection [J]. Image Processing, 2006, 15 (6) : 1454-1463.
  • 7ANIL K JAIN, ARUN ROSS, SALIL PRABHAKAR. An Introduction to Biometric Recognition [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2004, 14 ( 1 ) : 4-20.
  • 8NIINUMA K,PARK U ,JAIN A K. Soft biometric traits for continu- ous user authentication [ J ]. Information Forensics and Security. 2004,5(4) :771-780.
  • 9ZHOU XL,BHANU B. lnlegrating face and gait hr human recogni- tion at a distance in video[ J ]. IEEE Trans. System Man. and Cyber- netics,2007,37 ( 5 ) : 1119-1137.
  • 10GENG Xin, WANG Liang, LI Ming, et al. Adaptive fusion of gait and face for human identification in video [ J 1. Applications of Computer Vision ,2008,7 ( 9 ) : 1-6.

引证文献3

二级引证文献7

清华大学学报（自然科学版）
2008年 第2期

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