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首页> 外文会议>2010 IEEE International Conference on Progress in Informatics and Computing >Face detection in complex background based on skin color features and improved AdaBoost algorithms
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Face detection in complex background based on skin color features and improved AdaBoost algorithms

机译：基于肤色特征和改进的AdaBoost算法的复杂背景下的人脸检测

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摘要

Face detection is a very hot topic in research application of pattern recognition and computer vision. It is widely applied in artificial intelligence, video surveillance, identity authentication, human-machine interaction and so on. However, skin color detection has high false positive rate in complex background and AdaBoost algorithm was not satisfactory for detection of multi-pose and multi-face image. So a novel face detection method combined with skin color detection and an improved AdaBoost algorithm is proposed in this paper. First, it applies skin model segmentation and morphological operators to detect skin regions in the image. And according to the geometrical characteristics of the face, it screens the candidate face regions. Then by the improved classifiers in a cascade structure based on AdaBoost, it achieves more accurate promising regions of face. The experiment results show that this face detection algorithm improves the detection speed in both the quality of detection, and it can effectively reduce the error detection rate of single test method. This method has a good performance on image with complex background. Above all, this method has a certain theory value and practical value.
机译：人脸检测是模式识别和计算机视觉研究应用中的一个非常热门的话题。它广泛应用于人工智能，视频监控，身份认证，人机交互等方面。然而，肤色检测在复杂的背景下具有较高的假阳性率，AdaBoost算法对于多姿态和多面部图像的检测并不令人满意。因此，本文提出了一种结合肤色检测和改进的AdaBoost算法的人脸检测新方法。首先，它应用皮肤模型分割和形态运算符来检测图像中的皮肤区域。并根据脸部的几何特征筛选候选脸部区域。然后，通过基于AdaBoost的级联结构中的改进分类器，可以实现更准确的有前途的面部区域。实验结果表明，该人脸检测算法在提高检测质量的同时，提高了检测速度，可以有效降低单一检测方法的错误检测率。该方法在背景复杂的图像上具有良好的性能。最重要的是，该方法具有一定的理论价值和实用价值。

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