| Summary: | The performance of automated facial expression coding is improving steadily. Advances in deep learning techniques have been key to this success. While the advantage of modern deep learning techniques is clear, the contribution of critical design choices remains largely unknown, especially for facial action unit occurrence and intensity across pose. Using the The Facial Expression Recognition and Analysis 2017 (FERA 2017) database, which provides a common protocol to evaluate robustness to pose variation, we systematically evaluated design choices in pre-training, feature alignment, model size selection, and optimizer details. Informed by the findings, we developed an architecture that exceeds state-of-the-art on FERA 2017. The architecture achieved a 3.5% increase in F1 score for occurrence detection and a 5.8% increase in Intraclass Correlation (ICC) for intensity estimation. To evaluate the generalizability of the architecture to unseen poses and new dataset domains, we performed experiments across pose in FERA 2017 and across domains in Denver Intensity of Spontaneous Facial Action (DISFA) and the UNBC Pain Archive.
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