基于KPCA和SVM的人脸识别研究
首发时间:2010-11-15
摘要:为了满足企业管针对人脸识别中遇到的"过学习"与"小样本"问题,以及为了进一步改善主成分分析(PCA)在处理图像非线性问题上存在的不足,本文将核主成分分析法与支持向量机(KPCA-SVM)相结合,利用KPCA对非线性人脸图像进行特征提取,在保持原图像信息损失尽量少的原则下,把高维空间的人脸数据投影到低维空间,然后对低维空间人脸信息数据建立SVM的识别模型进行识别,达到95.4%的识别精度。
关键词: 核主成分分析 支持向量机 核函数 人脸识别 模式识别
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Face Recognition Study Based on Kernel Principal Component Analysis and Support Vector Machine
Abstract:A new approach for face recognition based on kernel principal component analysis (KPCA) and support vector machine (SVM) is presented to improve the recognition performance of principal component analysis (PCA), which has shortage in processing nonlinear image problems. Meanwhile, this method can be applied to solve both overfitting problem and small sample problem. The KPCA method is performed on every facial image of training set to get kernel facial features of training samples. Under the principle that the information of image loses as little as possible, the face data of high dimension feature space is shadowed into low dimensional space. The SVM face recognition model is established and then the low-dimensional space information data is identified. Experiment results demonstrate that the approach proposed in this paper is efficient, and recognition accuracy of the method proposed reaches 95.4%.
Keywords: KPCA SVM kernel function methods face recognition pattern recognition
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