Cascade adaboost classifiers with stage optimization for face detection

Authors:
Zongying Ou;Xusheng Tang;Tieming Su;Pengfei Zhao
Affiliations:
Key Laboratory for Precision and Non-traditional Machining Technology, of Ministry of Education, Dalian University of Technology, Dalian, P.R. China;Key Laboratory for Precision and Non-traditional Machining Technology, of Ministry of Education, Dalian University of Technology, Dalian, P.R. China;Key Laboratory for Precision and Non-traditional Machining Technology, of Ministry of Education, Dalian University of Technology, Dalian, P.R. China;Key Laboratory for Precision and Non-traditional Machining Technology, of Ministry of Education, Dalian University of Technology, Dalian, P.R. China
Venue:
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Year:
2006

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Abstract

In this paper, we propose a novel feature optimization method to build a cascade Adaboost face detector for real-time applications, such as teleconferencing, user interfaces, and security access control. AdaBoost algorithm selects a set of weak classifiers and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, the weights of weak classifiers may not be optimized. To address this issue, we proposed a novel Genetic Algorithm post optimization procedure for a given boosted classifier, which yields better generalization performance.