CatFLW: Cat Facial Landmarks in the Wild Dataset

TL;DR

This paper introduces CatFLW, a comprehensive dataset of 2016 cat face images with 48 landmarks, enabling automated facial analysis for understanding animal emotions and states, and presents a semi-supervised annotation method.

Contribution

The paper provides the largest annotated dataset of cat facial landmarks and a semi-supervised annotation approach to facilitate future animal facial analysis research.

Findings

Largest dataset of cat facial landmarks available

Semi-supervised annotation method reduces labeling time

Dataset supports research on animal emotional states

Abstract

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.