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

Authors:
Bo Wu;Haizhou Ai;Chang Huang;Shihong Lao
Affiliations:
Department of Computer Science and Technology, Tsinghua University, Beijing, China;Department of Computer Science and Technology, Tsinghua University, Beijing, China;Department of Computer Science and Technology, Tsinghua University, Beijing, China;Sensing Technology Laboratory, Omron Corporation
Venue:
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Year:
2004

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Abstract

In this paper, we propose a rotation invariant multiview face detection method based on Real Adaboost algorithm [1]. Human faces are divided into several categories according to the variant appearance from different view points. For each view category, weak classifiers are configured as confidence-rated look-uptable (LUT) of Haar feature [2]. 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 320×240 sized image. Experiments on faces with 360-degree inplane rotation and ±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+MIT 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.