北京航空航天大学学报 ›› 2006, Vol. 32 ›› Issue (06): 695-699.

• 论文 • 上一篇    下一篇

嵌入式隐Markov模型的分段训练方法

薛斌党1, 薛文芳2, 姜志国1   

  1. 1. 北京航空航天大学 宇航学院, 北京 100083;
    2. 中科院自动化研究所, 北京 100080
  • 收稿日期:2005-07-07 出版日期:2006-06-30 发布日期:2010-09-20
  • 作者简介:薛斌党(1971-), 男, 河南新野人, 讲师, xuebd@buaa.edu.cn.

Segmental training scheme for embedded hidden markov model

Xue Bindang1, Xue Wenfang2, Jiang Zhiguo1   

  1. 1. School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;
    2. Institute of Automation, Chinese Academy of Science, Beijing 100080, China
  • Received:2005-07-07 Online:2006-06-30 Published:2010-09-20

摘要: 针对嵌入式隐Markov模型再学习问题,提出了分段训练方法用于人脸识别:把当前的训练样本看作整体训练样本的一部分,训练结束后存储训练后的模型参数和中间变量;增加新样本后,以当前模型参数作为初始模型参数,用新增样本训练模型,得到新的中间变量,最后将已存储的中间变量和用新样本计算得到的中间变量合成,得到最终的模型.人脸识别实验结果表明了该方法的可行性.

Abstract: A segmental scheme to retrain E-HMM(embedded hidden Markov models) for face recognition was presented. The current samples were assumed to be a subset of the whole training samples, after the training process, the E-HMM parameters and the necessary temporary parameters in the parameter re-estimating process were saved for the use of next step. When new training samples were added, the trained E-HMM parameters were chosen as the initial parameters, the E-HMM was retrained based on the new samples and the new temporary parameters were obtained. These temporary parameters were combined with the saved temporary parameters to form the final E-HMM parameters so that one person face was presented. Experiments on face database showed that the segmental training method was effective.

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