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An efficient waveleteural networks-based face detection algorithm

机译:一种基于小波/神经网络的高效人脸检测算法

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In this paper, we proposed an efficient method to address the problem of face detection that is based on neural networks (NNs) and wavelet representation. We utilized a multilayer perceptron (MLP) so as to classify skin and non-skin pixels in the YCrCb color space. In this work, skin samples in images with varying lighting conditions are used to obtain a wide skin color distribution. The training data is generated from positive and negative training patterns in the Cb-Cr planes. Subsequently, training set is fed to an MLP, trained using the Levenberg-Marquardt algorithm using these skin samples. We apply the above mentioned NN-based skin classifier to the chrominance values corresponding to the coarsest level of the chrominance approximation subimages obtained from wavelet transform to classify the candidate face pixels. Furthermore, we have proposed a subspace approach in the space-frequency domain for the fast detection of face utilizing wavelet representation.
机译:在本文中,我们提出了一种基于神经网络和小波表示的有效方法来解决人脸检测问题。我们利用多层感知器(MLP)来对YCrCb颜色空间中的皮肤和非皮肤像素进行分类。在这项工作中,使用具有不同照明条件的图像中的皮肤样本来获得较宽的皮肤颜色分布。训练数据是根据Cb-Cr平面中的正负训练模式生成的。随后,将训练集馈送到MLP,并使用这些皮肤样本使用Levenberg-Marquardt算法对其进行训练。我们将上述基于NN的皮肤分类器应用于与从小波变换获得的色度近似子图像的最粗糙级别相对应的色度值,以对候选面部像素进行分类。此外,我们提出了一种在空频域中的子空间方法,用于利用小波表示快速检测人脸。

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