Face detection plays a crucial role in developing Human-Robot Interaction (HRI) for Intelligent Educational Robot (IER) to recognize users or speakers.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 Corner Cascade Classifier[1,2,23,29], and the real human face detection is improved to implement from single-face detection to multi-face detection.Furthermore, variable head pose is taken into account, such as pitch, roll, yaw, etc.The proposed robot vision algorithm for human detection is tested to be effective and robust through real-time experiments.%人脸检测在人机交互HRI(Human-Robot Interaction)过程中起着重要作用,它能够让智能教育机器人IER(Intelligent Educational Robot)识别用户或说话人.提出一种智能视觉算法,可以实时地从复杂的场景中检测出所出现的多个人脸,并过滤掉那些看起来像人脸但并不是人脸的图像.实时的人脸检测使用基于Adaboost的角点级联分类器[1,2,23,29],并对该检测算法进行了改进,以实现从单一人脸检测到多人脸检测.此外,改进的算法也考虑了各种可能的头部姿态,如俯仰、偏转、倾斜等.实验结果充分证明了所提出的机器人视觉算法进行人脸检测的鲁棒性和高效性.
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