A Probabilistic Fusion Approach to Human Age Prediction
Abstract
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.
Cite
Text
Guo et al. "A Probabilistic Fusion Approach to Human Age Prediction." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2008. doi:10.1109/CVPRW.2008.4563041Markdown
[Guo et al. "A Probabilistic Fusion Approach to Human Age Prediction." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2008.](https://mlanthology.org/cvprw/2008/guo2008cvprw-probabilistic/) doi:10.1109/CVPRW.2008.4563041BibTeX
@inproceedings{guo2008cvprw-probabilistic,
title = {{A Probabilistic Fusion Approach to Human Age Prediction}},
author = {Guo, Guodong and Fu, Yun and Dyer, Charles R. and Huang, Thomas S.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2008},
pages = {1-6},
doi = {10.1109/CVPRW.2008.4563041},
url = {https://mlanthology.org/cvprw/2008/guo2008cvprw-probabilistic/}
}