Hierarchical Facial Age Estimation Using Gaussian Process Regression

Automatic age estimation from facial images has attracted increasing attention due to its promising potential in real-life computer vision applications. However, due to uncontrollable environments, insufficient and incomplete training data, strong person-specific and large within- age span variations, age estimation has become a challenging problem. Among published age estimation, hierarchical age estimation methods have achieved comparable performance improvement than single level approaches. Most of the published hierarchical approaches have mainly used support vector machines to classify age groups followed by support vector regression for withina- age group age estimation. In this paper, we present a novel hierarchical Gaussian process framework for automatic age estimation. It consists of multi-class Gaussian process classifier to classify the input images into different age groups followed by a warped Gaussian process regression to model group specific aging patterns. In this paper, we separately tune the hyper-parameters for each age group at the regression stage. Compared with existing single level Gaussian process approaches for age estimation, our approach is computationally efficient at both the levels of hierarchy. Partitioning data into different age groups and learning group-wise hyper-parameters is computationally more efficient than learning complete training data. Misclassifications at the group boundaries are compensated at the regression stage by overlapping the neighboring age ranges. Finally, through extensive experiments on two popular aging datasets, the FG-NET and the Morph-II, we demonstrate the effectiveness of our algorithm in improving age estimation performance.