A Mixture of Personalized Experts for Human Affect Estimation
Date:
We investigate the personalization of deep convolutional neural networks for facial expression analysis from still images. While prior work has focused on population-based (``one-size-fits-all'') approaches, we formulate and construct personalized models via a mixture of experts and supervised domain adaptation approach, showing that it improves greatly upon non-personalized models. Our experiments demonstrate the ability of the model personalization to quickly and effectively adapt to limited amounts of target data. We also provide a novel training methodology and architecture for creating personalized machine learning models for more effective analysis of emotion state.