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

Authors :
Xu, Beibei
Wang, Wensheng
Guo, Leifeng
Chen, Guipeng
Li, Yongfeng
Cao, Zhen
Wu, Saisai
Xu, Beibei
Wang, Wensheng
Guo, Leifeng
Chen, Guipeng
Li, Yongfeng
Cao, Zhen
Wu, Saisai
Source :
ISSN: 0168-1699
Publication Year :
2022

Abstract

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.

Details

Database :
OAIster
Journal :
ISSN: 0168-1699
Notes :
application/pdf, Computers and Electronics in Agriculture 193 (2022), ISSN: 0168-1699, ISSN: 0168-1699, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1323245233
Document Type :
Electronic Resource