Open-Set Sheep Face Recognition in Multi-View Based on Li-SheepFaceNet

Jianquan Li, Ying Yang (), Gang Liu, Yuanlin Ning and Ping Song
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Jianquan Li: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Ying Yang: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Gang Liu: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Yuanlin Ning: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Ping Song: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2024, vol. 14, issue 7, 1-19

Abstract: Deep learning-based sheep face recognition improves the efficiency and effectiveness of individual sheep recognition and provides technical support for the development of intelligent livestock farming. However, frequent changes within the flock and variations in facial features in different views significantly affect the practical application of sheep face recognition. In this study, we proposed the Li-SheepFaceNet, a method for open-set sheep face recognition in multi-view. Specifically, we employed the Seesaw block to construct a lightweight model called SheepFaceNet, which significantly improves both performance and efficiency. To enhance the convergence and performance of low-dimensional embedded feature learning, we used Li-ArcFace as the loss function. The Li-SheepFaceNet achieves an open-set recognition accuracy of 96.13% on a self-built dataset containing 3801 multi-view face images of 212 Ujumqin sheep, which surpasses other open-set sheep face recognition methods. To evaluate the robustness and generalization of our approach, we conducted performance testing on a publicly available dataset, achieving a recognition accuracy of 93.33%. Deploying Li-SheepFaceNet on an open-set sheep face recognition system enables the rapid and accurate identification of individual sheep, thereby accelerating the development of intelligent sheep farming.

Keywords: sheep face recognition; computer vision; convolutional neural network; deep learning; intelligent livestock farming (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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