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

文献类型： 外文期刊

第一作者： Xu, Beibei

作者： Xu, Beibei;Wang, Wensheng;Guo, Leifeng;Li, Yongfeng;Cao, Zhen;Wu, Saisai;Chen, Guipeng;Cao, Zhen

作者机构：

关键词： Face recognition; RetinaFace; ArcFace loss; Deep learning; Precision livestock

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 193 卷

页码：

收录情况： SCI

摘要： 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.

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[1]CattleFaceNet: A cattle face identification approach based on RetinaFace and ArcFace loss. Xu, Beibei,Wang, Wensheng,Guo, Leifeng,Li, Yongfeng,Cao, Zhen,Wu, Saisai,Chen, Guipeng,Cao, Zhen. 2022

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