Happy Cow or Thinking Pig? WUR Wolf – Facial Coding Platform for Measuring Emotions in Farm Animals

Abstract

Emotions play an indicative and informative role in the investigation of farm animal behaviors. Systems that respond and can measure emotions provide a natural user interface in enabling the digitalization of animal welfare platforms. The faces of farm animals can be one of the richest channels for expressing emotions. We present WUR Wolf (Wageningen University & Research: Wolf Mascot)—a real-time facial expression recognition platform that can automatically code the emotions of farm animals. Using Python-based algorithms, we detect and track the facial features of cows and pigs, analyze the appearance, ear postures, and eye white regions, and correlate with the mental/emotional states of the farm animals. The system is trained on dataset of facial features of images of the farm animals collected in over 6 farms and has been optimized to operate with an average accuracy of 85%. From these, we infer the emotional states of animals in real time. The software detects 13 facial actions and 9 emotional states, including whether the animal is aggressive, calm, or neutral. A real-time emotion recognition system based on YoloV3, and Faster YoloV4-based facial detection platform and an ensemble Convolutional Neural Networks (RCNN) is presented. Detecting expressions of farm animals simultaneously in real time makes many new interfaces for automated decision-making tools possible for livestock farmers. Emotions sensing offers a vast amount of potential for improving animal welfare and animal-human interactions.