Face Detecting Using Artificial Neural Network Approach

S. A. Nazeer, Nazaruddin Omar, Khairol Faisal Jumari, M. Khalid
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引用次数: 20

Abstract

A frontal face detection system using artificial neural network is presented. The system used integral image for image representation which allows fast computation of the features used. The system also applies the AdaBoost learning algorithm to select a small number of critical visual features from a very large set of potential features. Besides that, it also used cascade of classifiers algorithm which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. Furthermore, a set of experiments in the domain of face detection is presented. The system yields a promising face detection performance
基于人工神经网络的人脸检测方法
提出了一种基于人工神经网络的正面人脸检测系统。该系统采用积分图像进行图像表示,可以快速计算所使用的特征。该系统还应用AdaBoost学习算法,从大量潜在特征中选择少量关键视觉特征。除此之外,它还使用了级联分类器算法,该算法允许快速丢弃图像的背景区域,而在有希望的类脸区域上花费更多的计算。在此基础上,提出了一组人脸检测领域的实验。该系统具有良好的人脸检测性能
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