  We propose a novel convolutional neural network approach to address the
fine-grained recognition problem of multi-view dynamic facial action unit
detection. We leverage recent gains in large-scale object recognition by
formulating the task of predicting the presence or absence of a specific action
unit in a still image of a human face as holistic classification. We then
explore the design space of our approach by considering both shared and
independent representations for separate action units, and also different CNN
architectures for combining color and motion information. We then move to the
novel setup of the FERA 2017 Challenge, in which we propose a multi-view
extension of our approach that operates by first predicting the viewpoint from
which the video was taken, and then evaluating an ensemble of action unit
detectors that were trained for that specific viewpoint. Our approach is
holistic, efficient, and modular, since new action units can be easily included
in the overall system. Our approach significantly outperforms the baseline of
the FERA 2017 Challenge, with an absolute improvement of 14% on the F1-metric.
Additionally, it compares favorably against the winner of the FERA 2017
challenge. Code source is available at https://github.com/BCV-Uniandes/AUNets.

Computer Vision and Pattern Recognition