  The development of existing facial coding systems, such as the Facial Action
Coding System (FACS), relied on manual examination of facial expression videos
for defining Action Units (AUs). To overcome the labor-intensive nature of this
process, we propose the unsupervised learning of an automated facial coding
system by leveraging computer-vision-based facial keypoint tracking. In this
novel facial coding system called the Data-driven Facial Expression Coding
System (DFECS), the AUs are estimated by applying dimensionality reduction to
facial keypoint movements from a neutral frame through a proposed Full Face
Model (FFM). FFM employs a two-level decomposition using advanced
dimensionality reduction techniques such as dictionary learning (DL) and
non-negative matrix factorization (NMF). These techniques enhance the
interpretability of AUs by introducing constraints such as sparsity and
positivity to the encoding matrix. Results show that DFECS AUs estimated from
the DISFA dataset can account for an average variance of up to 91.29 percent in
test datasets (CK+ and BP4D-Spontaneous) and also surpass the variance
explained by keypoint-based equivalents of FACS AUs in these datasets.
Additionally, 87.5 percent of DFECS AUs are interpretable, i.e., align with the
direction of facial muscle movements. In summary, advancements in automated
facial coding systems can accelerate facial expression analysis across diverse
fields such as security, healthcare, and entertainment. These advancements
offer numerous benefits, including enhanced detection of abnormal behavior,
improved pain analysis in healthcare settings, and enriched emotion-driven
interactions. To facilitate further research, the code repository of DFECS has
been made publicly accessible.
