Abstract: To solve problem of highly complexity of multi-angle face detection by the Adaboost algorithm based on the Haar-like, two new groups of extended Haar-like feature were proposed and the calculation method were exploited by the integral image. Then, the frontal faces cascaded classifier and the profile faces cascaded classifier was trained on the face database by the Adaboost algorithm respectively. Finally, the two-channel cascaded classifier was built. On OpenCV which is an open source vision database, the experimental results showed that the proposed method had better performance both in accuracy and computing speed, and could detect face with less weak classifiers. Meanwhile, the cascaded classifier had a good ability of robustness on detecting multi-angle face.

Key words: Adaboost algorithm, Haar-like feature, cascaded classifier, face detection, integral image, OpenCV