Hanwoo face-based individual identification using deep learning

doi:10.12972/jame.2026.6.2.2

All Issue

2026 Vol.6, Issue 2 Preview Page

Research Article

Hanwoo face-based individual identification using deep learning 딥러닝을 이용한 한우 안면 기반 개체 인식: 안태의¹, 조수현², 이대현^3,4*
Tae-eui An¹, Soo-Hyun Cho², Dae-Hyun Lee^3,4*; ¹충남대학교 스마트농업시스템기계공학과 학부생
²충남대학교 농업기계공학과 대학원생
³충남대학교 스마트농업시스템기계공학과 교수
⁴충남대학교 농업기계공학과 교수

¹Undergraduate Student, Department of Smart Agricultural System Mechanical Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea
²Graduate Student, Department of Agricultural Mechanical Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea
³Professor, Department of Smart Agricultural System Mechanical Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea
⁴Professor, Department of Agricultural Mechanical Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea

30 June 2026. pp. 61-74

PDF

Abstract

Individual identification in livestock farms is a fundamental technology for precision livestock management, history tracking, and productivity management. Conventional identification methods, such as ear tags, sensors, and RFID devices, require physical attachment to animals and have limitations related to device loss, damage, and management burden. To overcome these limitations, camera-based non-contact identification technologies have received increasing attention. However, in practical livestock environments, existing cattle are repeatedly shipped out and new cattle are continuously introduced, requiring a technology that can reflect newly added individuals in the identification system. Therefore, in this study, we proposed a deep learning-based individual registration and identification framework using Hanwoo facial images to register new cattle with a small number of facial images. The proposed framework learns facial features from existing cattle, generates representative features from registration images of new cattle, and identifies individuals by comparing the similarity between input image features and registered features. In addition, the effects of the number and viewpoint composition of registration images on new-cattle identification performance were analyzed. Results showed a test accuracy of 98.68% for the initial identification model, and the highest overall average accuracy and new-cattle accuracy were 94.8% and 91.0%, respectively. Although identification performance varied depending on registration conditions, the proposed framework confirmed that newly introduced cattle can be reflected in the identification system using only a small number of registration images. Based on the results, the proposed approach is expected to improve the field applicability and operational efficiency of non-contact Hanwoo identification systems.

Keywords

Precision livestock farming

Hanwoo

Face identification

Cattle registration

References

Andrew, W., Gao, J., Mullan, S. M., Campbell, N. W., Dowsey, A. W., & Burghardt, T. (2021). Visual identification of individual Holstein-Friesian cattle via deep metric learning. Computers and Electronics in Agriculture, 185, 106133.
Awad, A. I. (2016). From classical methods to animal biometrics: A review on cattle identification and tracking. Computers and Electronics in Agriculture, 123, 423-435.
Berckmans, D. (2017). General introduction to precision livestock farming. Animal Frontiers, 7(1), 6-11.
Jocher, G., Chaurasia, A., & Qiu, J. (2023). YOLO by Ultralytics. arXiv preprint arXiv:2305.18982.
Kang, M. H., & Oh, S. H. (2025). Research trends in livestock facial identification: a review. Journal of Animal Science and Technology, 67(1), 43-55.
KREI (Korea Rural Economic Institute). (2026). Agricultural outlook conference 2026 Chapter 4: Supply and demand trends and outlook for Korean native cattle, pigs, and dairy cattle. [In Korean]
Kyaw, P. P., Tin, P., Aikawa, M., Kobayashi, I., & Zin, T. T. (2025). Automatic cattle identification system based on color point cloud using hybrid PointNet++ Siamese network. Scientific Reports, 15, 21938.
Lee, T., Na, Y., Kim, B. G., Lee, S., & Choi, Y. (2023). Identification of individual Hanwoo cattle by muzzle pattern images through deep learning. Animals, 13(18), 2856.
Li, D., Li, B., Li, Q., Wang, Y., Yang, M., & Han, M. (2024). Cattle identification based on multiple feature decision layer fusion. Scientific Reports, 14, 26631.
Li, Z., & Hoiem, D. (2018). Learning without forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2935-2947.
Ma, R., Ali, H., Waqar, M. M., Kim, S. C., & Kim, H. (2025). Pig face open set recognition and registration using a decoupled detection system and dual-loss vision transformer. Animals, 15(5), 691.
Mahmud, M. S., Zahid, A., Das, A. K., Muzammil, M., & Khan, M. U. (2021). A systematic literature review on deep learning applications for precision cattle farming. Computers and Electronics in Agriculture, 187, 106313.
Masana, M., Liu, X., Twardowski, B., Menta, M., Bagdanov, A. D., & van de Weijer, J. (2023). Class-incremental learning: Survey and performance evaluation on image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), 5513-5533.
Meng, Y., Yoon, S., Han, S., Fuentes, A., Park, J., Jeong, Y., & Park, D. S. (2023). Improving known-unknown cattle’s face recognition for smart livestock farm management. Animals, 13(22), 3588.
Norton, T., Chen, C., Larsen, M. L. V., & Berckmans, D. (2019). Review: Precision livestock farming: Building ‘digital representations’ to bring the animals closer to the farmer. Animal, 13(12), 3009-3017.
Shojaeipour, A., Falzon, G., Kwan, P., Hadavi, N., Cowley, F. C., & Paul, D. (2021). Automated muzzle detection and biometric identification via few-shot deep transfer learning of mixed breed cattle. Agronomy, 11(12), 2365.
Tzutalin. (2015). LabelImg: Graphical image annotation tool. GitHub repository.
Wang, R., Gao, R., Li, Q., & Dong, J. (2023). Pig face recognition based on metric learning by combining a residual network and attention mechanism. Agriculture, 13(1), 144.
Xu, B., Wang, W., Guo, L., Chen, G., Li, Y., Cao, Z., & Wu, S. (2022). CattleFaceNet: A cattle face identification approach based on RetinaFace and ArcFace loss. Computers and Electronics in Agriculture, 193, 106675.

Information

Publisher :The Korean Society for Agricultural Machinery
Publisher(Ko) :한국농업기계학회
Journal Title :Journal of Agricultural Machinery Engineering
Journal Title(Ko) :농업기계공학
Volume : 6
No :2
Pages :61-74
DOI :https://doi.org/10.12972/jame.2026.6.2.2

[1] Andrew, W., Gao, J., Mullan, S. M., Campbell, N. W., Dowsey, A. W., & Burghardt, T. (2021). Visual identification of individual Holstein-Friesian cattle via deep metric learning. Computers and Electronics in Agriculture, 185, 106133.

[2] Awad, A. I. (2016). From classical methods to animal biometrics: A review on cattle identification and tracking. Computers and Electronics in Agriculture, 123, 423-435.

[3] Berckmans, D. (2017). General introduction to precision livestock farming. Animal Frontiers, 7(1), 6-11.

[4] Jocher, G., Chaurasia, A., & Qiu, J. (2023). YOLO by Ultralytics. arXiv preprint arXiv:2305.18982.

[5] Kang, M. H., & Oh, S. H. (2025). Research trends in livestock facial identification: a review. Journal of Animal Science and Technology, 67(1), 43-55.

[6] KREI (Korea Rural Economic Institute). (2026). Agricultural outlook conference 2026 Chapter 4: Supply and demand trends and outlook for Korean native cattle, pigs, and dairy cattle. [In Korean]

[7] Kyaw, P. P., Tin, P., Aikawa, M., Kobayashi, I., & Zin, T. T. (2025). Automatic cattle identification system based on color point cloud using hybrid PointNet++ Siamese network. Scientific Reports, 15, 21938.

[8] Lee, T., Na, Y., Kim, B. G., Lee, S., & Choi, Y. (2023). Identification of individual Hanwoo cattle by muzzle pattern images through deep learning. Animals, 13(18), 2856.

[9] Li, D., Li, B., Li, Q., Wang, Y., Yang, M., & Han, M. (2024). Cattle identification based on multiple feature decision layer fusion. Scientific Reports, 14, 26631.

[10] Li, Z., & Hoiem, D. (2018). Learning without forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2935-2947.

[11] Ma, R., Ali, H., Waqar, M. M., Kim, S. C., & Kim, H. (2025). Pig face open set recognition and registration using a decoupled detection system and dual-loss vision transformer. Animals, 15(5), 691.

[12] Mahmud, M. S., Zahid, A., Das, A. K., Muzammil, M., & Khan, M. U. (2021). A systematic literature review on deep learning applications for precision cattle farming. Computers and Electronics in Agriculture, 187, 106313.

[13] Masana, M., Liu, X., Twardowski, B., Menta, M., Bagdanov, A. D., & van de Weijer, J. (2023). Class-incremental learning: Survey and performance evaluation on image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), 5513-5533.

[14] Meng, Y., Yoon, S., Han, S., Fuentes, A., Park, J., Jeong, Y., & Park, D. S. (2023). Improving known-unknown cattle’s face recognition for smart livestock farm management. Animals, 13(22), 3588.

[15] Norton, T., Chen, C., Larsen, M. L. V., & Berckmans, D. (2019). Review: Precision livestock farming: Building ‘digital representations’ to bring the animals closer to the farmer. Animal, 13(12), 3009-3017.

[16] Shojaeipour, A., Falzon, G., Kwan, P., Hadavi, N., Cowley, F. C., & Paul, D. (2021). Automated muzzle detection and biometric identification via few-shot deep transfer learning of mixed breed cattle. Agronomy, 11(12), 2365.

[17] Tzutalin. (2015). LabelImg: Graphical image annotation tool. GitHub repository.

[18] Wang, R., Gao, R., Li, Q., & Dong, J. (2023). Pig face recognition based on metric learning by combining a residual network and attention mechanism. Agriculture, 13(1), 144.

[19] Xu, B., Wang, W., Guo, L., Chen, G., Li, Y., Cao, Z., & Wu, S. (2022). CattleFaceNet: A cattle face identification approach based on RetinaFace and ArcFace loss. Computers and Electronics in Agriculture, 193, 106675.

Journal of Agricultural Machinery Engineering ISSN:2799-8673(Print) 2799-8819(Online) 농업기계공학

All Issue