  Estimation of pain intensity from facial expressions captured in videos has
an immense potential for health care applications. Given the challenges related
to subjective variations of facial expressions, and operational capture
conditions, the accuracy of state-of-the-art DL models for recognizing facial
expressions may decline. Domain adaptation has been widely explored to
alleviate the problem of domain shifts that typically occur between video data
captured across various source and target domains. Moreover, given the
laborious task of collecting and annotating videos, and subjective bias due to
ambiguity among adjacent intensity levels, weakly-supervised learning is
gaining attention in such applications. State-of-the-art WSL models are
typically formulated as regression problems, and do not leverage the ordinal
relationship among pain intensity levels, nor temporal coherence of multiple
consecutive frames. This paper introduces a new DL model for weakly-supervised
DA with ordinal regression that can be adapted using target domain videos with
coarse labels provided on a periodic basis. The WSDA-OR model enforces ordinal
relationships among intensity levels assigned to target sequences, and
associates multiple relevant frames to sequence-level labels. In particular, it
learns discriminant and domain-invariant feature representations by integrating
multiple instance learning with deep adversarial DA, where soft Gaussian labels
are used to efficiently represent the weak ordinal sequence-level labels from
target domain. The proposed approach was validated using RECOLA video dataset
as fully-labeled source domain data, and UNBC-McMaster shoulder pain video
dataset as weakly-labeled target domain data. We have also validated WSDA-OR on
BIOVID and Fatigue datasets for sequence level estimation.
