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. WUR Wolf (Wageningen University & Research: Wolf Mascot), a real-time facial recognition platform that can automatically code the emotions of farm animals, is presented in this study. The developed Python-based algorithms detect and track the facial features of cows and pigs, analyze the appearance, ear postures, and eye white regions, and correlate these with the mental/emotional states of the farm animals. The system is trained on a dataset of facial features of images of farm animals collected in over six farms and has been optimized to operate with an average accuracy of 85%. From these, the emotional states of animals in real time are determined. The software detects 13 facial actions and an inferred nine emotional states, including whether the animal is aggressive, calm, or neutral. A real-time emotion recognition system based on YoloV3, a Faster YoloV4-based facial detection platform and an ensemble Convolutional Neural Networks (RCNN) is presented. Detecting facial features of farm animals simultaneously in real time enables many new interfaces for automated decision-making tools for livestock farmers. Emotion sensing offers a vast potential for improving animal welfare and animal–human interactions.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
The facial landmark detection software used is based on a series of points in relation to phenotypic features of the species in question, but it uses an older theory to attach the location of those points to emotional states
Several recent studies have clearly laid the foundation for the measurement of emotional states of farm animals based on their facial features such as ears, eyes, and orbital tightening (Table 1)
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In particular, precision livestock farming, and artificial intelligence have the potential to shape transformation in animal welfare [1]. Unlocking the full potential of automated measurement of mental and emotional states of farm animals through digitalization such as facial coding systems would help blur the lines between biological, physical, and digital technologies [1,2]. Facial analysis platforms have long been in use for various applications, such as password systems on smartphones, identification at international border checkpoints, identification of criminals [5], diagnosis of Turner syndrome [6], detection of genetic disorder phenotypes [7], as a potential diagnostic tool for Parkinson disease [8], measuring tourist satisfaction through emotional expressions [9], and quantification of customer interest during shopping [10].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
