In this letter, we aim to investigate whether laboratory rats' pain can be automatically assessed through their facial expressions. To this end, we began by presenting a publicly available dataset called RatsPain, consisting of 1,138 facial images captured from six rats that underwent an orthodontic treatment operation. Each rat' facial images in RatsPain were carefully selected from videos recorded either before or after the operation and well labeled by eight annotators according to the Rat Grimace Scale (RGS). We then proposed a novel deep learning method called PainSeeker for automatically assessing pain in rats via facial expressions. PainSeeker aims to seek pain-related facial local regions that facilitate learning both pain discriminative and head pose robust features from facial expression images. To evaluate the PainSeeker, we conducted extensive experiments on the RatsPain dataset. The results demonstrate the feasibility of assessing rats' pain from their facial expressions and also verify the effectiveness of the proposed PainSeeker in addressing this emerging but intriguing problem. The RasPain dataset can be freely obtained from https://github.com/xhzongyuan/RatsPain.

翻译：本文旨在探究能否通过面部表情自动评估实验大鼠的疼痛状态。为此，我们首先构建了一个公开数据集RatsPain，包含六只接受正畸手术大鼠的1,138张面部图像。每只大鼠的面部图像均精心选自手术前后录制的视频，并由八位标注员根据大鼠疼痛量表（Rat Grimace Scale, RGS）进行标注。我们随后提出了一种名为PainSeeker的新型深度学习方法，用于通过面部表情自动评估大鼠疼痛。PainSeeker旨在定位与疼痛相关的面部局部区域，以从表情图像中学习兼具疼痛判别性与头部姿态鲁棒性的特征。通过在RatsPain数据集上开展大量实验，结果证实了从大鼠面部表情评估疼痛的可行性，同时验证了PainSeeker在解决这一新兴且有趣问题中的有效性。RatsPain数据集可从https://github.com/xhzongyuan/RatsPain免费获取。