Automated Detection of Cat Facial Landmarks
This work addresses the scarcity of high-quality datasets for animal affective computing, specifically for cats, enabling more accurate facial expression analysis in this domain.
The paper tackles the problem of analyzing cat facial expressions by creating a novel dataset of cat facial images annotated with 48 landmarks and developing a CNN-based model with a magnifying ensemble method, achieving excellent performance on cat faces and generalizability to human facial detection.
The field of animal affective computing is rapidly emerging, and analysis of facial expressions is a crucial aspect. One of the most significant challenges that researchers in the field currently face is the scarcity of high-quality, comprehensive datasets that allow the development of models for facial expressions analysis. One of the possible approaches is the utilisation of facial landmarks, which has been shown for humans and animals. In this paper we present a novel dataset of cat facial images annotated with bounding boxes and 48 facial landmarks grounded in cat facial anatomy. We also introduce a landmark detection convolution neural network-based model which uses a magnifying ensembe method. Our model shows excellent performance on cat faces and is generalizable to human facial landmark detection.