  Disentangling facial movements from other facial characteristics,
particularly from facial identity, remains a challenging task, as facial
movements display great variation between individuals. In this paper, we aim to
characterize individual-specific facial movements. We present a novel training
approach to learn facial movements independently of other facial
characteristics, focusing on each individual separately. We propose
self-supervised Person-Specific Models (PSMs), in which one model per
individual can learn to extract an embedding of the facial movements
independently of the person's identity and other structural facial
characteristics from unlabeled facial video. These models are trained using
encoder-decoder-like architectures. We provide quantitative and qualitative
evidence that a PSM learns a meaningful facial embedding that discovers
fine-grained movements otherwise not characterized by a General Model (GM),
which is trained across individuals and characterizes general patterns of
facial movements. We present quantitative and qualitative evidence that this
approach is easily scalable and generalizable for new individuals: facial
movements knowledge learned on a person can quickly and effectively be
transferred to a new person. Lastly, we propose a novel PSM using curriculum
temporal learning to leverage the temporal contiguity between video frames. Our
code, analysis details, and all pretrained models are available in Github and
Supplementary Materials.
