Abstract
In view of the slow speed and time-consuming training problem of the human face detection in complex conditions, we put forward an improved algorithm. To counter the time-consuming training defect of the Adaboot algorithm, we improve the about error rate calculation formula while training the weak classifier, thus accelerating the training speed of the latter and reducing the overall training time. The experimental results show that the improved system has greatly improved the training speed.
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References
Viola P, Jones M (2001) Robust Real Time Object Detection. Proceedings of 2nd international work shop on statistical and computational theories of vision
Xie H, Fang X, Cai Q (2009) A new weak classifier training method for Adaboost algorithm. J Image Graph 14(11):2411–2415
Cheng Sheng TU, Li Li D (2003) The typical algorithm of Adaboost series in boosting family. Comput Sci 30(03):30–35
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE conference on computer vision and pattern recognition, Kauai, Hawai, USA
Liang LH, Ai HZ, Xu GY, Zhang B (2002) A survey of human face detection. Chin J Comput 25(05):449–458
Freund Y, Schapire R (1999) A short introduction to boosting. J Jpn Soc Artif Intell 14(5):771–780
Wei D, Li L (2006) Improvement of Adaboost faces detection. Comput Appl 26(3):619–621
Xiong SW, Zong XL, Zhu GF (2005/2006) Improved face detection method based on AdaBoost algorithm. Appl Res Comput
Luo MG (2006) The design and implementation of the face detection system based on Adaboost algorithm. Kunming University of Science and Technology
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© 2013 Springer-Verlag Berlin Heidelberg
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Liao, S., Chen, Y. (2013). Improved Weak Classifier Optimization Algorithm. In: Yin, Z., Pan, L., Fang, X. (eds) Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013. Advances in Intelligent Systems and Computing, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37502-6_127
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DOI: https://doi.org/10.1007/978-3-642-37502-6_127
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