Abstract
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.
Abstract (translated)
动物情感计算领域正在迅速崛起，而面部表情的分析是一个关键方面。目前该领域研究人员面临的一个最显著的挑战是高质量、全面的数据集的稀缺性，这使得开发面部表情分析模型变得困难。一种可能的解决方案是利用面部标志点，这在人类和动物身上已经被证明是有效的。在本文中，我们介绍了一个由边界框和基于猫面部解剖学结构的48个面部标志点注释的猫面部图像的新型数据集。我们还介绍了一种使用卷积神经网络-based模型的地标检测方法。我们的模型在猫面部表现出色，并且可以扩展到人类面部地标检测。
URL
https://arxiv.org/abs/2310.09793
