  Despite their continued popularity, categorical approaches to affect
recognition have limitations, especially in real-life situations. Dimensional
models of affect offer important advantages for the recognition of subtle
expressions and more fine-grained analysis. We introduce a simple but effective
facial expression analysis (FEA) system for dimensional affect, solely based on
geometric features and Partial Least Squares (PLS) regression. The system
jointly learns to estimate Arousal and Valence ratings from a set of facial
images. The proposed approach is robust, efficient, and exhibits comparable
performance to contemporary deep learning models, while requiring a fraction of
the computational resources.
