Facial age estimation with bilateral relationships exploitation

Abstract

In the environment of smart cities, human facial age estimation has become an important research topic due to its wide applications. Although a variety of methods are proposed to depict facial biologic attributes, the underlying relationships between facial appearance and its biological aging process have not been fully explored. Although relationship learning methods have been constructed in terms of modeling either the facial representations or the facial attributes, none of them model the relationships simultaneously. In this paper, we propose a unified model to explore these relationships simultaneously, coined as JREAE. In JREAE, two covariance matrices are symmetrically constructed to capture the underlying correlations from both aspects of input facial features and output age labels. In this way, the potential relationships of both feature and label are not only modeled definitely, but also explored adaptively from the training data, which is significantly different from those methods that define the relationships manually. Then, we extend the JREAE model with deep convolutional architecture (deep-JREAE) for more powerful discrimination. In addition, we present optimization algorithms to solve the proposed models with theoretical convergence and complexity proof. Finally, through extensive experiments, we not only validate the effectiveness and superior of the proposed methods in performance, but also visualize and analyze the resulting relationships.