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.

翻译：动物情感计算领域正迅速兴起，面部表情分析是其关键环节。当前研究人员面临的主要挑战之一是缺乏高质量、全面的数据集，以支持面部表情分析模型的开发。一种可行的方法是使用面部关键点，这一方法已在人类和动物研究中得到验证。本文提出一个基于猫面部解剖学标注的新颖猫面部图像数据集，包含边界框和48个面部关键点。同时，我们介绍了一种基于卷积神经网络的关键点检测模型，该模型采用放大集成方法。本模型在猫面部检测中表现卓越，并可推广至人类面部关键点检测任务。