  Animal affective computing is a quickly growing field of research, where only
recently first efforts to go beyond animal tracking into recognizing their
internal states, such as pain and emotions, have emerged. In most mammals,
facial expressions are an important channel for communicating information about
these states. However, unlike the human domain, there is an acute lack of
datasets that make automation of facial analysis of animals feasible.
  This paper aims to fill this gap by presenting a dataset called Cat Facial
Landmarks in the Wild (CatFLW) which contains 2016 images of cat faces in
different environments and conditions, annotated with 48 facial landmarks
specifically chosen for their relationship with underlying musculature, and
relevance to cat-specific facial Action Units (CatFACS). To the best of our
knowledge, this dataset has the largest amount of cat facial landmarks
available.
  In addition, we describe a semi-supervised (human-in-the-loop) method of
annotating images with landmarks, used for creating this dataset, which
significantly reduces the annotation time and could be used for creating
similar datasets for other animals.
  The dataset is available on request.
