Systematic Evaluation of Design Choices for Deep Facial Action Coding Across Pose

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.