Hybrid constraint SVR for facial age estimation

In this paper, facial age estimation is discussed in a novel viewpoint – how to jointly exploit the supervised training data and human annotations to improve the age estimation precision. This is motivated by the lacking of data problem in age estimation and the current web booming. To do so, fuzzy age label is firstly defined, and it is then merged into the Support Vector Regression (SVR) framework together with the traditional data labels. The new learning problem is finally formulated into a similar dual form with the standard SVR, which can be easily solved using existing solvers. In experiments, we have compared with the state of the art regression based methods, and the results are very competitive.