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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Ou, Z., Tang, X., Su, T., & Zhao, P. (2006). Cascade AdaBoost classifiers with stage optimization for face detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3832 LNCS, pp. 121–128). https://doi.org/10.1007/11608288_17
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