Facial age estimation using clustered multi-task support vector regression machine
Peter Xiang Gao · 2011
Automatic age estimation is the process of using a computer to predict the age of a person automatically based on a given facial image. While this problem has numerous real-world applications, the high variabil-ity of aging patterns and the sparsity of available data present challenges for model training. Here, instead of training one global aging function, we train an individ-ual function for each person by a multi-task learning approach so that the variety of human aging processes can be modelled. To deal with the sparsity of train-ing data, we propose a similarity measure for clustering the aging functions. During the testing stage, which in-volves a new person with no data used for model train-ing, we propose a feature-based similarity measure for characterizing the test case. We conduct simulation ex-periments on the FG-NET and MORPH databases and compared our method with other state-of-the-art meth-ods. 1