Face recognition based on cross-validation by support vector machine

被引：0

作者：

Hu, Jingfang ^{[1
]}

You, Lin ^{[1
]}

机构：

[1] College of Communication Engineering, Hangzhou Dianzi University, Hangzhou

来源：

Lecture Notes in Electrical Engineering | 2015年 / 334卷

关键词：

Cross-validation; Face recognition; Principal component analysis; Support vector machine;

D O I：

10.1007/978-3-319-13707-0_111

中图分类号：

学科分类号：

摘要：

Support vector machine (SVM) is a kind of machine learning based on statistical learning theory. It shows unique advantages in the small-sample, nonlinear, and high-dimension pattern recognition. Principal component analysis (PCA) is a multivariate analysis technology for feature extraction. In this chapter, we propose a new method of face recognition based on PCA and SVM. It applies PCA to extract face feature and uses SVM combined with cross-validation (CV) to classify face images. CV is a good method of parameter optimization in SVM. We conduct the recognition experiment on the Cambridge ORL database. Compared with other methods, the accuracy rate of face recognition is up to 89.5 %. It is shown to be an effective method. © Springer International Publishing Switzerland 2015.

引用

页码：1011 / 1018

页数：7

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