  The Facial Action Coding System (FACS) for studying facial expressions is
manual and requires significant effort and expertise. This paper explores the
use of automated techniques to generate Action Units (AUs) for studying facial
expressions. We propose an unsupervised approach based on Principal Component
Analysis (PCA) and facial keypoint tracking to generate data-driven AUs called
PCA AUs using the publicly available DISFA dataset. The PCA AUs comply with the
direction of facial muscle movements and are capable of explaining over 92.83
percent of the variance in other public test datasets (BP4D-Spontaneous and
CK+), indicating their capability to generalize facial expressions. The PCA AUs
are also comparable to a keypoint-based equivalence of FACS AUs in terms of
variance explained on the test datasets. In conclusion, our research
demonstrates the potential of automated techniques to be an alternative to
manual FACS labeling which could lead to efficient real-time analysis of facial
expressions in psychology and related fields. To promote further research, we
have made code repository publicly available.
