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.

动物情感计算领域的重要挑战之一是缺乏高质量的数据集，本文提出了一个以猫脸图像为基础的新数据集，并通过使用面部标志检测卷积神经网络模型，在猫脸上取得了优异性能，同时该模型也具有推广到人脸标志检测的潜力。

猫脸部关键点的自动检测

Automated Detection of Cat Facial Landmarks

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.

本文介绍了一个名为 Cat Facial Landmarks in the Wild (CatFLW) 的数据集，其中包含了 2016 张猫脸的照片，并且使用 48 个脸部重点标记和猫脸特定的脸部动作单元（CatFACS）进行了注释，以用于动物情感计算和面部分析的自动化。此外，作者还介绍了一种半监督（人在环路）的方法来注释图像中的标志，以便创建此数据集，并提供在请求时获得该数据集。