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基于段匹配差异观察值的HMM在线签名认证方法研究 被引量：1

Study on Application of HMM to Online Signature Verification Based on Differences of Matched Segment
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摘要 提出一种用签名的分段差异值作为隐马尔可夫模型(HMM)观测值的在线签名认证应用方法.首先,采用双向后向合并DTW算法确定签名中关键点之间的对应关系.然后,采用经典DTW度量签名中各种细微的差异,用这些DTW差异值作为观测值训练HMM模型.将模型状态的意义定义为相似程度,将状态转移结构设定为全概率转移.在SVC2004签名数据库上,验证了该方法的有效性. An approach of hidden markov model （HMM） to online signature verification is proposed, which uses difference values obtained by segmentation dynamic time wrapping （DTW） as observations of model. Firstly, the correspondences of the critical points in signatures are made by bidirectional backward-merging dynamic time wrapping algorithm. Then, the subtle differences are calculated by classical dynamic time wrapping algorithm. These differences are utilized to train the HMM. The meanings of models states are defined as degrees of similarity, and the HMM topology is ergodic. The validity of the proposed approach is verified on SVC2004 signatures database.
作者 邹杰 吴仲城
机构地区 中国科学院合肥物质科学研究院强磁场中心控制实验室 中国科学院合肥智能机械研究所
出处 《模式识别与人工智能》 EI CSCD 北大核心 2011年第4期555-560,共6页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金项目资助(No.60975057)
关键词 在线签名认证 动态时间规整(DTW) 隐马尔可夫模型(HMM) Online Signature Verification, Dynamic Time Wrapping （DTW）, Hidden Markov Model（HMM）
分类号 TN918.2 [电子电信—通信与信息系统]
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参考文献19

  • 1Plamondon R, Lorette G. Automatic Signature Verification and Writer Identification - The State of the Art. Pattern Recognition, 1989, 22 (2) : 107 - 131.
  • 2Jain A K, Griess F D, Connell S D. On-Line Signature Verification. Pattern Recognition, 2002, 35 (12) : 2963 - 2972.
  • 3Sakoe H, Chiba S. Dynamic Programming Algorithm Optimization for Spoken Word Recognition. IEEE Trans on Acoustics, Speech and Signal Processing, 1978, 26( 1 ) : 43 -49.
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  • 9袁余良,沈峰,杨飞,潘金贵.一个基于HMM的动态手写签名认证系统的设计与实现[J].模式识别与人工智能,2004,17(2):146-150. 被引量：5
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二级参考文献24

  • 1Plamondon R, Lorette G. Automatic Signature Verification and Writer Identification - the State of the Art. Pattern Recognition Letters, 1989, 22(2): 107-131.
  • 2Feng Hao, Wah C C. Online Signature Verification Using a New Extreme Points Warping Technique. Pattern Recognition Letters, 2003, 24(16) : 2943 -2951.
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  • 6Lei Hansheng, Govindaraju V. A Comparative Study on the Consistency of Features in On-Line Signature Verification. Pattern Recognition Letters, 2005, 26 ( 15 ) : 2483 - 2489.
  • 7Jain A K, Griess F D, Connell S D. On-Line Signature Verification. Pattern Recognition, 2002, 35 (12) : 2963 - 2972.
  • 8Guru D S, Prakash H N. Online Signature Verification and Recognition: An Approach Based on Symbolic Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31 (6) : 1059 - 1073.
  • 9Draouhard J P, Sabourin R, Godbout M. A Neural Network Approaches to On-Line Signature Verification Using Directional PDF. Pattern Recognition, 1996, 29 ( 3 ) : 415 - 424.
  • 10Bajaj R, Chaudhary S. Signature Verification Using Multiple Neural Classifiers. Pattern Recognition, 1997, 30(1 ) : 1 -7.

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模式识别与人工智能
2011年 第4期

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