Adaptive learning for celebrity identification with video context

In this paper, we propose a novel semi-supervised learning strategy to address the problem of celebrity identification. The video context information is explored to facilitate the learning process based on the assumption that faces in the same video track share the same identity. Once a frame within a track is recognized confidently, the label can be propagated through the whole track, referred to as the confident track. More specifically, given a few static images and vast face videos, an initial weak classifier is trained and gradually evolves by iteratively promoting the confident tracks into the 'labeled' set. The iterative selection process enriches the diversity of the 'labeled' set such that the performance of the classifier is gradually improved. This learning theme may suffer from semantic drifting caused by errors in selecting the confident tracks. To address this issue, we propose to treat the selected frames as related samples - an intermediate state between labeled and unlabeled instead of labeled as in the traditional approach. To evaluate the performance, we construct a new dataset, which includes 3000 static images and 2700 face tracks of 30 celebrities. Comprehensive evaluations on this dataset and a public video dataset indicate significant improvement of our approach over established baseline methods.