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A Robust Face Detector Algorithm Utilizing Neural Networks and Partial Template Matching

机译:利用神经网络和部分模板匹配的鲁棒人脸检测算法

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Face detection from an arbitrary scene has become a very actively studied topic in the image processing and pattern recognition fields. The reason for the importance of face detection is in its broad applications, for example in human detection by means of visual input for security reason, human-machine interaction, and video archiving. Human face is composed from several components, each with large varieties and it can take many postures in arbitrary scene, which make detection task a very difficult one. In this study we propose a method for robust face detection from arbitrary scene utilizing neural network as face's posture predictor and partial template matching of human face. The proposed model is robust to the lighting conditions and postures of the frontal faces.
机译:从任意场景进行面部检测已经成为图像处理和模式识别领域中非常活跃的研究主题。人脸检测重要性的原因在于它的广泛应用,例如,出于安全原因,人机交互和视频存档,通过视觉输入进行人检测。人脸由多个部分组成,每个部分种类繁多,并且可以在任意场景中采取许多姿势,这使检测任务非常困难。在这项研究中,我们提出了一种利用神经网络作为人脸的姿态预测器和人脸部分模板匹配的从任意场景中进行鲁棒性人脸检测的方法。所提出的模型对于照明条件和正面的姿势是鲁棒的。

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