Pose-Informed Face Alignment for Extreme Head Pose Variations in Animals

Landmark localisation is a vital step in automatic analysis of facial expressions of animals. Head motion is one of the most challenging problems for face alignment for humans and animals. For animals this is exacerbated by the increased amounts of self-occlusion resulting from variations in head pose. In this paper, we present a novel model for detection of an extensive set of facial landmarks for sheep. A dataset of 850 sheep facial images, annotated with a 25 facial landmark scheme and occlusion information, is introduced: the Sheep Facial Landmarks in the Wild (SFLW) dataset, including a wide range of variations in head-pose and occlusion. Data augmentation techniques are introduced using thin-plate-spline warping and negatively correlated augmentation to boost representation of extreme head poses. We then present a novel pose-informed landmark localisation method based on a fine-tuned CNN model for human head pose estimation. This method is shown to significantly outperform the existing state-of-the-art approach on the introduced SFLW dataset and the viability of the technique for real-world use is demonstrated through the implementation of a near real-time video pipeline.