This paper addresses the need for quality datasets in animal affective computing, focusing on cat facial expressions. It introduces CatFLW (Cat Facial Landmarks in the Wild), a dataset of 2091 cat facial images annotated with bounding boxes and 48 facial landmarks based on cat anatomy and the Cat Facial Action Coding System. The paper also presents the Ensemble Landmark Detector (ELD), a CNN model that outperforms other models on the CatFLW dataset and generalizes well to humans and other animals. This work advances automated pain and emotion recognition in cats by providing an explainable approach that connects landmark geometry to action units, overcoming limitations of manual analysis and “black box” deep learning methods.
Non-Invasive Computer Vision-Based Fruit Fly Larvae Differentiation: Ceratitis capitata and Bactrocera zonata
This paper proposes a novel, non-invasive method using computer vision
