Segmentation and graph generation of muzzle images for cattle identification

  • Lucas Wojcik UFPR
  • Jorge Junior UFPR
  • David Menotti UFPR
  • João Hill IDR-Paraná


The current methods for the organizing the records (i.e., cataloguing) of cattle are known to be archaic and inefficient, and often harmful to the animal. Such methods include the use of metal tags attached to the animal's ears like earrings and of branding irons on their necks. Previous research on new methods of livestock branding based on computer vision techniques utilized a mixture of texture features such as Gabor Filters and Local Binary Pattern as a means of extracting identifying features for each animal. The presented approach proposes a new technique using the muzzle image as an individual identifier as a novel technique, assuming that the muzzle RoI taken as input for the model pipeline is already extracted and cropped. This task is performed in three steps. First, the muzzle image is segmented via a convolutional neural network, resulting in a bitmap from which a graph structure is extracted in the second phase. The final phase consists of matching the resulting graph with the ones previously extracted and stored in the database for an optimal match. The results for the segmentation quality show a fidelity of around seventy percent, while the extracted graph perfectly represents the extracted bitmap. The matching algorithm is currently in progress.


T. Burghardt, N. Campbell, P. J. Barham, I. C. Cuthill, and R. Sherley, “A fully automated computer vision system for the biometric identification of african penguins (spheniscus demersus) on robben island,” in 6th International Penguin Conference (IPC07). University of Tasmania, Australia, 2007.

D.-H. Jang, K.-S. Kwon, J.-K. Kim, K.-Y. Yang, and J.-B. Kim, “Dog identification method based on muzzle pattern image,” Applied Sciences, vol. 10, no. 24, p. 8994, 2020.

S. Kumar and S. Singh, “Automatic identification of cattle using muzzle point pattern: a hybrid feature extraction and classification paradigm,” Multimedia Tools and Applications, vol. 76, pp. 1–30, 12 2017.

A. Noviyanto and A. M. Arymurthy, “Beef cattle identification based on muzzle pattern using a matching refinement technique in the sift method,” Computers and Electronics in Agriculture, vol. 99, pp. 77–84, 2013. [Online]. Available:

A. Noviyanto and A. Arymurthy, “Automatic cattle identification based on muzzle photo using speed-up robust features approach,” Proceedings of the 3rd European Conference of Computer Science, pp. 110–114, 01 2012.

W. Kusakunniran and T. Chaiviroonjaroen, “Automatic cattle identification based on multi-channel lbp on muzzle images,” in 2018 International Conference on Sustainable Information Engineering and Technology (SIET), 2018, pp. 1–5.

T. Gaber, A. Tharwat, A. E. Hassanien, and V. Snasel, “Biometric cattle identification approach based on weber’s local descriptor and adaboost classifier,” Computers and Electronics in Agriculture, vol. 122, pp. 55–66, 2016. [Online]. Available:

W. Pedersen, “The identification of the bovine by means of nose prints,” J. Dairy Sci, vol. 5, p. 249–258.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention. Springer, 2015, pp. 234–241.

J.-J. Liu, Q. Hou, M.-M. Cheng, J. Feng, and J. Jiang, “A simple poolingbased design for real-time salient object detection,” in IEEE CVPR, 2019.

S. Beucher and F. Meyer, The Morphological Approach to Segmentation: The Watershed Transformation. Marcel Dekker Inc., 01 1993, vol. Vol. 34, p. 433–481.

D. Bradley and G. Roth, “Adaptive thresholding using the integral image,” Journal of graphics tools, vol. 12, no. 2, pp. 13–21, 2007.

T. Y. Zhang and C. Y. Suen, “A fast parallel algorithm for thinning digital patterns,” Communications of the ACM, vol. 27, no. 3, pp. 236– 239, 1984.

D. S. Bolme, “Elastic bunch graph matching,” Ph.D. dissertation, Colorado State University, 2003.
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WOJCIK, Lucas; JUNIOR, Jorge; MENOTTI, David; HILL, João. Segmentation and graph generation of muzzle images for cattle identification. In: WORKSHOP DE TRABALHOS EM ANDAMENTO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 34. , 2021, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 169-172. DOI: