Identity management based on PCA and SVM
Identity management based on PCA and SVM
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DOI：
10.1007/s10796-015-9551-8
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发表时间：
2015-04
影响因子：
5.9
通讯作者：
Lixin Shen;Hong Wang;Lida Xu;Xue Ma;S. Chaudhry;Wu He
中科院分区：
文献类型：
--
作者：
Lixin Shen;Hong Wang;Lida Xu;Xue Ma;S. Chaudhry;Wu He
A new approach for face recognition, based on kernel principal component analysis (KPCA) and support vector machines (SVMs), is presented to improve the recognition performance of the method based on principal component analysis (PCA). This method can simultaneously be applied to solve both the over-fitting problem and the small sample problem. The KPCA method is performed on every facial image of the training set to get the core facial features of the training samples. To ensure that the loss of the image information will be as less as possible, the facial data of high-dimensional feature space is projected into low-dimensional space, and then the SVM face recognition model is established to identify the low-dimensional space facial data. Our experimental results demonstrate that the approach proposed in this paper is efficient, and the recognition accuracy of the proposed method reaches 95.4 %.
