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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
A Probabilistic Fusion Approach to human age prediction
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Guodong Guo, Computer Science, NCCU, Durham, NC, 27707 USA
Yun Fu, Beckman Institute, UIUC, Urbana, IL 61801 USA
Charles R. Dyer, Computer Sciences, UW-Madison, WI, 53706 USA
Thomas S. Huang, Beckman Institute, UIUC, Urbana, IL 61801 USA
Human age prediction is useful for many applications. The age information could be used as a kind of semantic knowledge for multimedia content analysis and understanding. In this paper we propose a Probabilistic Fusion Approach (PFA) that produces a high performance estimator for human age prediction. The PFA framework fuses a regressor and a classifier. We derive the predictor based on Bayes’ rule without the mutual independence assumption that is very common for traditional classifier combination methods. Using a sequential fusion strategy, the predictor reduces age estimation errors significantly. Experiments on the large UIUC-IFP-Y aging database and the FG-NET aging database show the merit of the proposed approach to human age prediction.
Citation:
Guodong Guo, Yun Fu, Charles R. Dyer, Thomas S. Huang, "A Probabilistic Fusion Approach to human age prediction," cvprw, pp.1-6, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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