A Probabilistic Fusion Approach to Human Age Prediction
G-D. Guo, Y. Fu, C. R. Dyer and T. S. Huang, Proc. 3rd International Workshop on Semantic Learning and Applications in Multimedia, 2008.

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.