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首页> 外文会议>Mechatronics and its Applications, 2009. ISMA '09 >Face detection based on dimension reduction using probabilistic neural network and Genetic Algorithm
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Face detection based on dimension reduction using probabilistic neural network and Genetic Algorithm

机译：基于概率神经网络和遗传算法的降维人脸检测

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Past work on face detection has emphasized the issues of feature extraction and classification, however, less attention has been given on the critical issue of feature selection. We consider the problem of face and non-face classification from frontal facial images using feature selection and neural networks. We argue that feature selection is an important issue in face and non-face classification. Automatic feature subset selection distinguishes the proposed method from previous face classification approaches. First, principal component analysis (PCA) is used to represent each image as a feature vector (i.e., eigen-features) in a low-dimensional space, spanned by the eigenvectors of the covariance matrix of the training images (i.e., coefficients of the linear expansion).Then we consider linear discrimination analysis (LDA) to achieve a comparison result between these two methods of dimension reduction. Genetic algorithm (GA) is then used to select a subset of features from the low-dimensional representation by removing certain eigenvectors that do not seem to encode important information about face. Finally, a probabilistic neural network (PNN) is trained to perform face classification using the selected eigen-feature subset. Experimental results demonstrate a significant improvement in error rate reduction.
机译：过去有关人脸检测的工作强调了特征提取和分类的问题，但是，对特征选择这一关键问题的关注却很少。我们考虑使用特征选择和神经网络从正面人脸图像中进行人脸和非人脸分类的问题。我们认为特征选择是人脸和非人脸分类中的重要问题。自动特征子集选择将提出的方法与以前的面部分类方法区分开。首先，主成分分析（PCA）用于将每个图像表示为低维空间中的特征向量（即特征特征），并由训练图像的协方差矩阵的特征向量（即然后，我们考虑使用线性判别分析（LDA）来获得这两种降维方法的比较结果。遗传算法（GA）然后用于通过删除某些似乎不编码有关面部重要信息的特征向量来从低维表示中选择特征子集。最后，训练概率神经网络（PNN）以使用选定的特征特征子集执行面部分类。实验结果表明，错误率降低方面有显着改善。

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