Automated Detection of Cat Facial Landmarks

Abstract

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.

arXiv commentarXiv admin note: substantial text overlap with arXiv:2305.04232

References in corpus (8)

• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Deep Facial Expression Recognition: A Survey
• Deep learning tools for the measurement of animal behavior in neuroscience
• Facial Landmark Detection: a Literature Survey
• Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
• Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning
• Leg-tracking and automated behavioral classification in Drosophila
• Structured Landmark Detection via Topology-Adapting Deep Graph Learning