PainSeeker: An Automated Method for Assessing Pain in Rats Through
  Facial Expressions
Liu Liu Guang Li Dingfan Deng Jinhua Yu Yuan Zong
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.