Co-Incrementation: Combining Co-Training and Incremental Learning for Subject-Specific Facial Expression Recognition

Jordan Gonzalez, Thibault Geoffroy, Aurelia Deshayes, Lionel Prevost

Abstract: In this work, we propose to adapt a generic emotion recognizer to a set of individuals in order to improve its accuracy. As this adaptation is weakly supervised, we propose a hybrid framework, the so-called co-incremental learning that combines semi-supervised co-training and incremental learning. The classifier we use is a specific random forest whose internal nodes are nearest class mean classifiers. It has the ability to learn incrementally data covariate shift. We use it in a co-training process by combining multiple view of the data to handle unlabeled data and iteratively learn the model. We performed several personalization and provided a comparative study between these models and their influence on the co-incrementation process. Finally, an in-depth study of the behavior of the models before, during and after the co-incrementation process was carried out. The results, presented on a benchmark dataset, show this hybrid process increases the robustness of the model, with only a