CattleFaceNet: A cattle face identification approach based on RetinaFace and ArcFace loss

Cattle identification is crucial to be registered for breeding association, food quality tracing, disease prevention and control and fake insurance claims. Traditional non-biometrics methods for cattle identification is not really satisfactory in providing reliability due to theft, fraud, and duplication. In this study, a computer vision technique was proposed to facilitate precision animal management and improve livestock welfare. This paper presents a novel face identification framework by integrating light-weight RetinaFace-mobilenet with Additive Angular Margin Loss (ArcFace), namely CattleFaceNet. RetinaFace-mobilenet is designed for face detection and location, and ArcFace is adopted to strengthen the within-class compactness and also between-class discrepancy during training. Experiments on real-word scenarios dataset prove that RetinaFace-mobilenet achieves superior detection performance and significantly accelerates the computation time against RetinaNet. Three loss functions utilized in human face recognition combined with RetinaFace-mobilenet are compared and results indict that the proposed CattleFaceNet outperforms others with identification accuracy of 91.3% and processing time of 24 frames per second (FPS). This research work demonstrates the potential candidate of CattleFaceNet for livestock identification in real time in practical production scenarios.

Automated summary

In this study, a novel cattle identification framework named CattleFaceNet was proposed, which integrated RetinaFace-mobilenet for face detection and location, and ArcFace for improving the within-class compactness and between-class discrepancy during training. The experimental results showed that CattleFaceNet outperformed other methods in terms of identification accuracy and processing time, demonstrating its potential for real-time livestock identification in practical production scenarios.