Abstract
A novel variant of AdaBoost named AcoBoost is proposed to directly solve the asymmetric constraint optimization problem for cascade face detector using a two-stage feature selection approach. In the first stage, many candidate features are picked out by minimizing the weighted error. In the second stage, the optimal feature is singled out by minimizing the asymmetric constraint error. By doing so, the convergence rate is greatly speeded up. Besides, a new sample set called selection set is added into AcoBoost to prevent overfitting on the training set, which ensures good enough generalization ability for AcoBoost. The experimental results on building several upright frontal cascade face detectors show that the AcoBoost based classifiers have much better convergence ability and slightly worse generalization ability than the AdaBoost based ones. Some AcoBoost based cascade face detectors have satisfactory performance on the CMU+MIT upright frontal face test set.
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Wen, JB., Xiong, YS. (2012). Asymmetric Constraint Optimization Based Adaptive Boosting for Cascade Face Detector. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_30
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DOI: https://doi.org/10.1007/978-3-642-25944-9_30
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