A Multi-View Face Detection Based on Real Adaboost Algorithm

In this paper, a multi-view face detection method based on real Adaboost algorithm is presented. Human faces are divided into several viewpoint categories according to their poses in 3D, and for each of these categories a form of weak classifiers in look-up-table (LUT) type is designed using Haar-like features that have confidences in real values as their outputs, and correspondingly its space of weak classifiers is constructed, from which the cascade face detector is learnt by using real Adaboost algorithm. For speed up, multi-resolution searching and pose prediction strategies are introduced. For frontal face detection, the experiments on CMU+MIT frontal face test set result in a correct rate of 94.5% with 57 false alarms; for multi-view face detection, the experiments on CMU profile face test set result in a correct rate of 89.8% with 221 false alarms. The average processing time on a PⅣ 2.4GHz PC is about 80 ms for a 320×240-pixel image. It can be seen that the proposed method is very efficient and has significant value in application.