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计算机工程 ›› 2010, Vol. 36 ›› Issue (7): 193-194. doi: 10.3969/j.issn.1000-3428.2010.07.066

• 人工智能及识别技术 • 上一篇    下一篇

基于独立成分分析和核向量机的人脸识别

彭中亚,程国建   

  1. (西安石油大学计算机学院,西安 710065)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-04-05 发布日期:2010-04-05

Face Recognition Based on Independent Component Analysis and

PENG Zhong-ya, CHENG Guo-jian   

  1. (School of Computer Science, Xi’an Shiyou University, Xi’an 710065)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-04-05 Published:2010-04-05

摘要: 提出利用独立成分分析提取人脸特征并用核向量机进行识别的方法。独立成分分析能更本质地描述图像特征,通过选择合适的特征个数达到较高的识别准确率。利用核向量机进行分类判决,可以快速地对大样本数据进行准确分类,产生较少的支持向量。实验证明了该方法的可行性和有效性,在ORL人脸数据库上达到了94.38%的准确率。

关键词: 人脸识别, 独立成分分析, 核向量机, 支持向量机

Abstract: This paper proposes an algorithm which adopts Independent Component Analysis(ICA) to extract face feature and Core Vector Machines(CVM) to recognize. ICA is used to extract statistical independent feature and a good result can be received by selecting right feature numbers. CVM is used to classify the feature and it can handle large data sets more quickly. Experimental results show that the algorithm is feasible and effective for face recognition, and its accuracy is 94.38% on ORL.

Key words: face recognition, Independent Component Analysis(ICA), Core Vector Machines(CVM), Support Vector Machines(SVM)

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