Gentle Adaboost algorithm based on multi-feature fusion for face detection

There are few types of Haar-like rectangle features, which leads to the problem that the classifier training time is too long due to the large number of feature quantities required in the description of the face. Local binary patterns (LBP) are used to describe the local texture features of the face image. Considering the inadequacy of basic LBP features in face detection, unified MB-LBP features and unified rotation-invariant LBP features are used to describe local texture features of faces. Considering the shortcomings of MB-LBP feature and rotation-invariant LBP feature on face edge information, the edge azimuth field feature based on Canny operator is combined with the above two features to describe face information. Finally, the Gentle Adaboost classifier was designed to classify all the extracted features. The experimental results show that the unified MB-LBP feature and the unified rotation invariant LBP feature and the edge azimuth field feature based on Canny operator can not only describe the face information from the local but also the whole, which greatly improves the detection rate and detection speed of the face with multiple poses and different rotation modes.