Abstract
Cascades of boosted ensembles have become a popular technique for face detection following their introduction by Viola and Jones. Researchers have sought to improve upon the original approach by incorporating new techniques such as alternative boosting methods, feature sets, etc. We explore several avenues that have not yet received adequate attention: global cascade learning, optimal ensemble construction, stronger weak hypotheses, and feature filtering. We describe a probabilistic model for cascade performance and its use in a fully-automated training algorithm.
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Brubaker, S.C., Wu, J., Sun, J., Mullin, M.D., Rehg, J.M. (2006). Towards the Optimal Training of Cascades of Boosted Ensembles. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol 4170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11957959_16
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DOI: https://doi.org/10.1007/11957959_16
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