• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2010, Vol. 32 ›› Issue (6): 58-60.doi: 10.3969/j.issn.1007130X.2010.

• 论文 • 上一篇    下一篇

基于增量学习SVM的人脸识别

吕俊亚1,韩忠军2   

  1. (1.河南财经学院计算机与信息工程学院,河南 郑州 450002;2.郑州航空工业管理学院实验室管理处,河南 郑州 450015)
  • 收稿日期:2009-09-13 修回日期:2009-12-10 出版日期:2010-06-01 发布日期:2010-06-01
  • 通讯作者: 吕俊亚 E-mail:lvjunya0303@126.com
  • 作者简介:吕俊亚(1970),男,河南许昌人,硕士,讲师,研究方向为数据库技术和人工智能;韩忠军,硕士,讲师,研究方向为计算机应用。

Face Recognition Based on the Incremental  Learning Support Vector Machine

L Junya1,HAN Zhongjun2   

  1. (1.School of Computer and Information Engineering,Henan Institute of Finance and Economics,Zhengzhou 450002;
    2.Laboratory Management Section,Zhengzhou Institute of Aeronautical Industry Management,Zhengzhou 450015,China)
  • Received:2009-09-13 Revised:2009-12-10 Online:2010-06-01 Published:2010-06-01

摘要:

为了提高人脸识别率,本文提出了一种增量学习支持矢量机(SVM)人脸识别方法,有效地对SVM的参数进行更新。提出的方法采用高斯概率模型描述SVM的参数统计特征,在无需额外存储训练数据的前提下,采用增量学习SVM的方式实现参数的更新;并通过最小化分类误差准则最大化SVM两类输出值概率分布间的距离。详细的实验以及与现有方法的比较结果表明,提出的识别方法具有更好的识别性能。

关键词: 增量学习, 支持矢量机, 人脸识别

Abstract:

To improve the face recognition rate, this paper proposes an incremental learning support vector machine (SVM) face recognition scheme to update the parameters of SVM efficiently. The proposed scheme adopts the Gaussian probability model to depict the parameters of SVM, and updates the parameters of SVM based on the incremental learning SVM without saving the training data. The proposed scheme also employs the rule that minimizes the error of classifications to maximize the distance of the output distributions of two classes. The detailed experimental results and comparisons with the existing schemes show that the proposed scheme can obtain better recognition performance.

Key words: incremental learning;support vector machine;face recognition

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