Facial Age Estimation Using a Multi-Task Network Combining Classification and Regression

Age estimation of facial images is very challenging because of the complexity of face aging process and the difficulty of collecting and labeling data. A holistic regression model is subject to imbalanced training data, while a divide-and-conquer method highly depends on the effect of the age classification, which usually has boundary effect due to cross-age correlations. This paper proposes a simple but effective multi-task learning (MTL) network combining classification and regression for age estimation called CR-MT net, where classification acts as an auxiliary task to regression. MTL can boost the generalization performance of the age regression task by shared information representation learning of the two tasks. Compared to divide-and-conquer methods, our method performs MTL for two tasks with an end-to-end training, and has no error propagation from classification to regression. The holistic regression model in CR-MT net does not meet the boundary effect, and can fit the heterogeneous or unbalanced age data more accurately with the aid of a good age data partition in classification. We evaluate two age grouping techniques to find a good data partition, and diagnose various factors which can influence the performance of the CR-MT net by extensive experiments. The CR-MT net is verified in three public datasets, and achieves state-of-art results.