猫面部关键点的自动检测
Automated Detection of Cat Facial Landmarks
AI总结：
针对动物情感计算领域缺乏高质量面部分析数据集的问题，本文构建了标注48个关键点的猫面部图像数据集，并提出基于放大集成的卷积神经网络关键点检测模型，在猫脸检测上表现优异且可迁移至人脸检测。
摘要：
动物情感计算领域正快速兴起，面部表情分析是其中的关键方向。该领域研究者当前面临的最重大挑战之一，是缺乏可用于开发面部表情分析模型的高质量、综合性数据集。可行的研究路径之一是利用面部关键点，这一方法已在人类和动物研究中得到验证。本文提出了一个全新的猫面部图像数据集，标注有边界框以及基于猫面部解剖学的48个面部关键点。我们还提出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。我们的模型在猫面部检测任务上表现优异，且可迁移应用于人类面部关键点检测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
