FEA-Accu cascade for face detection

Authors:
Shengye Yan;Shiguang Shan;Xilin Chen;Wen Gao
Affiliations:
Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing, China and Digital Media Research Center, Institute of Computing Technology, CAS, Beijing, China and Graduate Sc ...;Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing, China and Digital Media Research Center, Institute of Computing Technology, CAS, Beijing, China;Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing, China and Digital Media Research Center, Institute of Computing Technology, CAS, Beijing, China;School of EE&CS, Peking University, Beijing, China and Digital Media Research Center, Institute of Computing Technology, CAS, Beijing, China
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

Aiming at unloading the high training time burden of the popular cascaded classifier, in this paper, a novel cascade structure called Fea-Accu cascade is proposed. In Fea-Accu cascade training, the times of feature selection are largely reduced by enhancing the correlation among different stage classifiers of the cascaded classifier. In detail, for each stage classifier, before selecting new features out, the features selected out by previous stage classifiers are reused through creating new corresponding weak classifiers. To verify the efficiency and effectiveness of the proposed method, experiment is designed on frontal face detection problem. The experimental results show that it can largely reduce the training time. A frontal face detector with state-of-the-art classification performance can be learned in less than 10 hours.