  In the recent years, there has been a shift in facial behavior analysis from
the laboratory-controlled conditions to the challenging in-the-wild conditions
due to the superior performance of deep learning based approaches for many real
world applications.However, the performance of deep learning approaches relies
on the amount of training data. One of the major problems with data acquisition
is the requirement of annotations for large amount of training data. Labeling
process of huge training data demands lot of human support with strong domain
expertise for facial expressions or action units, which is difficult to obtain
in real-time environments.Moreover, labeling process is highly vulnerable to
ambiguity of expressions or action units, especially for intensities due to the
bias induced by the domain experts. Therefore, there is an imperative need to
address the problem of facial behavior analysis with weak annotations. In this
paper, we provide a comprehensive review of weakly supervised learning (WSL)
approaches for facial behavior analysis with both categorical as well as
dimensional labels along with the challenges and potential research directions
associated with it. First, we introduce various types of weak annotations in
the context of facial behavior analysis and the corresponding challenges
associated with it. We then systematically review the existing state-of-the-art
approaches and provide a taxonomy of these approaches along with their insights
and limitations. In addition, widely used data-sets in the reviewed literature
and the performance of these approaches along with evaluation principles are
summarized. Finally, we discuss the remaining challenges and opportunities
along with the potential research directions in order to apply facial behavior
analysis with weak labels in real life situations.
