Rio de Janeiro, Brazil
Oct. 14, 2007 to Oct. 21, 2007
Rong Xiao , Visual Computing Group, Microsoft Research Asia, Beijing, P. R. China. email@example.com
Huaiyi Zhu , Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, P. R. China. firstname.lastname@example.org
He Sun , Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, P. R. China. email@example.com
Xiaoou Tang , Visual Computing Group, Microsoft Research Asia, Beijing, P. R. China. firstname.lastname@example.org
In this paper, we propose a novel method, called "Dynamic Cascade", for training an efficient face detector on massive data sets. There are three key contributions. The first is a new cascade algorithm called "Dynamic Cascade", which can train cascade classifiers on massive data sets and only requires a small number of training parameters. The second is the introduction of a new kind of weak classifier, called "Bayesian Stump", for training boost classifiers. It produces more stable boost classifiers with fewer features. Moreover, we propose a strategy for using our dynamic cascade algorithm with multiple sets of features to further improve the detection performance without significant increase in the detector's computational cost. Experimental results show that all the new techniques effectively improve the detection performance. Finally, we provide the first large standard data set for face detection, so that future researches on the topic can be compared on the same training and testing set.
Rong Xiao, Huaiyi Zhu, He Sun, Xiaoou Tang, "Dynamic Cascades for Face Detection", ICCV, 2007, Computer Vision, IEEE International Conference on, Computer Vision, IEEE International Conference on 2007, pp. 1-8, doi:10.1109/ICCV.2007.4409043