Ranking Model for Facial Age Estimation

Feature design and feature selection are two key problems in facial image based age perception. In this paper, we proposed to using ranking model to do feature selection on the haar-like features. In order to build the pairwise samples for the ranking model, age sequences are organized by personal aging pattern within each subject. The pairwise samples are extracted from the sequence of each subject. Therefore, the order information is intuitively contained in the pairwise data. Ranking model is used to select the discriminative features based on the pairwise data. The combination of the ranking model and personal aging pattern are powerful to select the discriminative features for age estimation. Based on the selected features, different kinds of regression models are used to build prediction models. The experiment results show the performance of our method is comparable to the state-of-art works.