Improved AdaBoost Algorithm for Face Detection and Its Application

Abstract

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 using the approach called AdaBoost-based Haar-Cascade Classifier[1-2], 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.