Witness detection in multi-instance regression and its application for age estimation

The huge data resource on the Web provides us with an emerging chance to solve the lack of training sample problem that lasting for years in facial age estimation. We show that the web images assisted age estimation can be modeled into a Multiple Instance Regression (MIR) problem. Different from the traditional Multiple Instance Learning (MIL) problem that deals with the bag-level classification task, we model the age estimation problem as an instance-level task. To this end, it is essential to reveal the latent instance labels from all bags. In this paper, we propose a novel algorithm named Witness Detecting Multi-instance Regression (WDMR) that can find all possible positive instances from training bags and use them to train an instance-level regressor. Considering the connection between neighbor relationship and inter-/intra- class differences among instances, we develop a Supervised Citer k-Nearest Neighbor (SC-kNN) graph and a sparse voting strategy to address these problems within a joint learning framework. Experimental results on synthetic and real-world datasets have verified the advantages of our method compared with other state-of-the-art MIL approaches.