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CattleFaceNet: A cattle face identification approach based on RetinaFace and ArcFace loss.
- Source :
-
Computers & Electronics in Agriculture . Feb2022, Vol. 193, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- • A novel cattle face identification framework for practical production scenarios. • RetinaFace-mobilenet is designed for cattle face detection and location. • ArcFace Loss is combined with RetinaFace-mobilenet applied for individual identification. • The framework outperforms other competing algorithms and achieves accuracy of 91.3%. 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01681699
- Volume :
- 193
- Database :
- Academic Search Index
- Journal :
- Computers & Electronics in Agriculture
- Publication Type :
- Academic Journal
- Accession number :
- 154995599
- Full Text :
- https://doi.org/10.1016/j.compag.2021.106675