Abstract
With the development of information technology, the research on face detection has been an important topic in computer vision. In this paper, a novel method is proposed for face detection based on Cost-Gentle Adaboost algorithm. The main differences between our method and the traditional Gentle Adaboost are that the cost factors have been introduced into the training process: the higher the value, the more important of this class samples. In the new training process, the selected classifiers can more effectively focus on the face samples than the traditional Gentle Adaboost algorithm. The face detector trained by our method can achieve higher detection rate at appropriate false positive rates. Experimental results also show that our method is effective.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China under Projects 61201271 and Specialized Research Fund for the Doctoral Program of Higher Education 20100185120021.
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Cheng, J., Liu, H., Wang, J., Li, H. (2014). Face Detection Based on Cost-Gentle Adaboost Algorithm. In: Zhang, B., Mu, J., Wang, W., Liang, Q., Pi, Y. (eds) The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-00536-2_31
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DOI: https://doi.org/10.1007/978-3-319-00536-2_31
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