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Improved AdaBoost Algorithm for Robust Real-Time Multi-face Detection

机译：改进的AdaBoost算法用于鲁棒实时多人脸检测

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Face detection is the basis for research topics such as face recognition, facial expression recognition and face attribute analysis and it plays a crucial role in the field of computer vision. Traditional methods are difficult to meet the needs of robust real-time multi face detection because of some influencing factors such as head pose, image scene, illumination condition and so on. In this paper, we introduce an intelligent vision algorithm that is able to detect human face from complex scene and filter out all the non-face but face-like images. The human face is detected in real-time environment using the approach called Adaboost-based Haar-Cascade Classifier, and the real human face detection is improved from single-face detection to multi-face detection. In addition, variable head poses are taken into account, such as pitch, roll, yaw, etc. Furthermore, the real-time experiments proved the effectiveness and robustness of the algorithm for human detection we have proposed.
机译：面部检测是诸如面部识别，面部表情识别和面部属性分析等研究主题的基础，并且在计算机视觉领域起着至关重要的作用。由于头部姿态，图像场景，照明条件等一些影响因素，传统方法难以满足鲁棒实时多人脸检测的需求。在本文中，我们介绍了一种智能视觉算法，该算法能够从复杂场景中检测人脸并过滤掉所有非人脸但类似人脸的图像。使用基于Adaboost的Haar-Cascade分类器的方法在实时环境中检测人脸，并将真实人脸检测从单人脸检测改进为多人脸检测。此外，还考虑了可变的头部姿势，例如俯仰，侧倾，偏航等。此外，实时实验证明了我们提出的人类检测算法的有效性和鲁棒性。

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