  Facial expression analysis is an important tool for human-computer
interaction. In this paper, we introduce LibreFace, an open-source toolkit for
facial expression analysis. This open-source toolbox offers real-time and
offline analysis of facial behavior through deep learning models, including
facial action unit (AU) detection, AU intensity estimation, and facial
expression recognition. To accomplish this, we employ several techniques,
including the utilization of a large-scale pre-trained network, feature-wise
knowledge distillation, and task-specific fine-tuning. These approaches are
designed to effectively and accurately analyze facial expressions by leveraging
visual information, thereby facilitating the implementation of real-time
interactive applications. In terms of Action Unit (AU) intensity estimation, we
achieve a Pearson Correlation Coefficient (PCC) of 0.63 on DISFA, which is 7%
higher than the performance of OpenFace 2.0 while maintaining highly-efficient
inference that runs two times faster than OpenFace 2.0. Despite being compact,
our model also demonstrates competitive performance to state-of-the-art facial
expression analysis methods on AffecNet, FFHQ, and RAF-DB. Our code will be
released at https://github.com/ihp-lab/LibreFace
