Fast rotation invariant multi-view face detection based on real Adaboost

In this paper, we propose a rotation invariant multi-view face detection method based on Real Adaboost algorithm. Human faces are divided into several categories according to the variant appearance from different viewpoints. For each view category, weak classifiers are configured as confidence-rated look-up-table (LUT) of Haar feature. Real Adaboost algorithm is used to boost these weak classifiers and construct a nesting-structured face detector. To make it rotation invariant, we divide the whole 360-degree range into 12 sub-ranges and construct their corresponding view based detectors separately. To improve performance, a pose estimation method is introduced and results in a processing speed of four frames per second on 320/spl times/240 sized image. Experiments on faces with 360-degree in-plane rotation and /spl mnplus/90-degree out-of-plane rotation are reported, of which the frontal face detector subsystem retrieves 94.5% of the faces with 57 false alarms on the CMU+MlT frontal face test set and the multi-view face detector subsystem retrieves 89.8% of the faces with 221 false alarms on the CMU profile face test set.