Abstract
In this paper, we propose a novel method called Multi-Feature Soft Cascade Learning for improving the performance of face detection. The main contribution of this paper consists of the following two aspects. The first is the use of Multi-Feature in AdaBoost, resulting in a more stable boost classifier with fewer features compared with using only single features as well as the improvement of the detection performance. The second is the new soft cascade algorithm for the Multi-Feature training, which works together with the Multi-Feature selection criterion. Experiment results show the improvement by using Multi-Feature compared with single feature. We also find that the candidate feature set is another important factor to improve the face detection performance.
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© 2008 Springer-Verlag Berlin Heidelberg
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Zhu, H., Zhang, L., Sun, H., Xiao, R. (2008). Face Detection Using Multi-Feature. In: Wang, R., Shen, E., Gu, F. (eds) Advances in Cognitive Neurodynamics ICCN 2007. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8387-7_160
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DOI: https://doi.org/10.1007/978-1-4020-8387-7_160
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-8386-0
Online ISBN: 978-1-4020-8387-7
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