Towards automatic monitoring of disease progression in sheep: A hierarchical model for sheep facial expressions analysis from video

Abstract

Pain in farm animals harms the economics of farming and affects animal welfare. However, prey animals tend to not openly express signs of weakness, making the pain assessment process difficult. We propose a novel hierarchical model for disease progression evaluation, adapted for a wide range of head poses, according to which relevant information is extracted. A fine-tuned CNN is applied for face detection, followed by a CNN-based pose estimation and pose-informed landmark location method. Then multi-modal features are extracted, combining the appearance of regions-of-interest, described using a Histogram of Oriented Gradients, with geometric features and the pose values, leading to a binary Support Vector Machine classifier. To evaluate the efficiency of the complete pipeline, videos of the same sheep recorded at initial and advanced stages of treatment were tested, showing a decrease in the average pain score detected. The pain evaluation method significantly outperformed the existing state-of-the-art approach, being the first to apply a pose-based feature extraction in sheep pain detection.