Automated Landmark-Based Cat Facial Analysis and its Applications

Abstract Facial landmarks, widely studied in human affective computing, are beginning to gain interest in the animal domain. Specifically, landmark-based geometric morphometric methods have been used to objectively assess facial expressions in cats, focusing on pain recognition and the impact of breed-specific morphology on facial signaling. These methods employed a 48-landmark scheme grounded in cat facial anatomy. Manually annotating these landmarks, however, is a labour intensive process, deeming it impractical for generating sufficiently large amounts of data for machine learning purposes as well as use in applied real time contexts with cats. Our previous work introduced an AI pipeline for automated landmark detection, which showed good performance in standard machine learning metrics. Nonetheless, the effectiveness of fully automated, end-to-end landmark-based systems for practical cat facial analysis tasks remained underexplored. This paper presents a thorough evaluation of such systems for three benchmark tasks related to cat facial analysis: recognition of breed, cephalic type, and pain.