Attention-based multi-modal robust cattle identification technique using deep learning

Abstract

Conventional cattle identification techniques, such as ear tagging, tattooing, microchip embedding, notch-based, and electrical marking, have two main limitations. First, they are potentially distressing to animals. Second, they are susceptible to duplication or loss, which makes them unreliable. Over the past few years, research has revealed that cattle, much like humans, possess numerous unique biometric traits. Additionally, advancements in computing technology over the past decade have significantly enhanced innovations in digitizing animal biometrics. In this context, our study aims to investigate the amalgamation of multiple biometric modalities in cattle, using their face and muzzle patterns to establish a distinctive identification technique. Once cattle biometrics are digitized, they may find practical applications in resolving ownership assignment disputes, dealing with fraudulent insurance claims, and smart livestock management systems. It can also serve as a foundation for establishing regulatory compliance frameworks. The proposed technique in this paper presents a novel approach involving (1) precise detection of the face and muzzle by training the YOLOv8-based object detector, (2) feature extraction by fine-tuning the pre-trained deep convolutional neural network, (3) enhancing feature extraction by introducing spatial attention, (4) classification based on multi-modal features and (5) result analysis with various ablation studies and explainability with Grad-CAM. The proposed attention-based multi-modal identification technique incorporates both facial and muzzle cues and demonstrates a robust identification accuracy of 99.47% for muzzle features alone and a combined identification accuracy of 99.64% using both face and muzzle features. © 2025 Elsevier B.V., All rights reserved.