Abstract
The Equine Facial Action Coding System (EquiFACS) enables the systematic annotation of facial movements through distinct Action Units (AUs). It serves as a crucial tool for assessing affective states in horses by identifying subtle facial expressions associated with discomfort. However, the field of horse affective state assessment is constrained by the scarcity of annotated data, as manually labelling facial AUs is both time-consuming and costly. To address this challenge, automated annotation systems are essential for leveraging existing datasets and improving affective states detection tools. In this work, we study different methods for specific ear AU detection and localization from horse videos. We leverage past works on deep learning-based video feature extraction combined with recurrent neural networks for the video classification task, as well as a classic optical flow based approach. We achieve 87.5% classification accuracy of ear movement presence on a public horse video dataset, demonstrating the potential of our approach. We discuss future directions to develop these systems, with the aim of bridging the gap between automated AU detection and practical applications in equine welfare and veterinary diagnostics. Our code will be made publicly available at this https URL.
Abstract (translated)
《马面部动作编码系统（EquiFACS）》允许通过特定的动作单元（AUs，Action Units）对马的面部运动进行系统的标注。它作为评估马的情绪状态的重要工具，在识别与不适相关的小幅面部表情方面发挥着关键作用。然而，由于缺乏标注数据，马情绪状态评估领域的研究受到了限制，因为手动标记面部动作单位既耗时又昂贵。为了应对这一挑战，自动注释系统对于利用现有数据集和改进情绪状态检测工具至关重要。 在本项工作中，我们研究了不同方法，用于从马视频中识别和定位特定的耳朵AUs。我们结合深度学习基线视频特征提取与递归神经网络应用于视频分类任务，并且采用了一种经典的基于光学流的方法。我们在一个公共马视频数据集中实现了87.5%的耳部运动存在分类准确率，展示了我们方法的潜力。 我们将讨论未来的发展方向，旨在弥合自动AUs检测和在赛马福利及兽医诊断中的实际应用之间的差距。我们的代码将在以下链接公开提供：[此 URL]。
URL
https://arxiv.org/abs/2505.03554
