  The field of animal affective computing is rapidly emerging, and analysis of
facial expressions is a crucial aspect. One of the most significant challenges
that researchers in the field currently face is the scarcity of high-quality,
comprehensive datasets that allow the development of models for facial
expressions analysis. One of the possible approaches is the utilisation of
facial landmarks, which has been shown for humans and animals. In this paper we
present a novel dataset of cat facial images annotated with bounding boxes and
48 facial landmarks grounded in cat facial anatomy. We also introduce a
landmark detection convolution neural network-based model which uses a
magnifying ensembe method. Our model shows excellent performance on cat faces
and is generalizable to human facial landmark detection.
