Face detection and grimace scale prediction of white furred mice

Abstract

Studying the facial expressions of humans has been one of the major applications of computer vision. An open question is whether common machine learning techniques can also be used to track behaviors of animals, which is a less explored research problem. Since animals are not capable of verbal communication, computer vision solutions can provide valuable information to track the animal’s state. We are particularly interested in pain neurobiology research, where rodent models are extensively used to investigate pain interventions. A grimace scale is used to understand the suffering of a mouse in the presence of interventions, which is inferred from various facial features such as the shape of the eyes and ears. In this work, we automate the prediction of the grimace scale on white furred mice using a machine learning approach, following the same principles used for human facial expression recognition: face detection, landmark region extraction, and expression recognition. We demonstrate the use of the you only look once (YOLO) framework for face detection of the mice with outstanding results. For eye region extraction and grimace pain prediction, we propose a novel structure based on a dilated convolutional network. The experimental results are promising, showing that it is possible to differentiate among the pain scale of the mice.