Cattle Identification Using Muzzle Print Images Based on Texture Features Approach

  • Alaa Tharwat
  • Tarek Gaber
  • Aboul Ella Hassanien
  • Hasssan A. Hassanien
  • Mohamed F. Tolba
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)


The increasing growth of the world trade and growing concerns of food safety by consumers need a cutting-edge animal identification and traceability systems as the simple recording and reading of tags-based systems are only effective in eradication programs of national disease. Animal biometric-based solutions, e.g. muzzle imaging system, offer an effective and secure, and rapid method of addressing the requirements of animal identification and traceability systems. In this paper, we propose a robust and fast cattle identification approach. This approach makes use of Local Binary Pattern (LBP) to extract local invariant features from muzzle print images. We also applied different classifiers including Nearest Neighbor, Naive Bayes, SVM and KNN for cattle identification. The experimental results showed that our approach is superior than existed works as ours achieves 99,5% identification accuracy. In addition, the results proved that our proposed method achieved this high accuracy even if the testing images are rotated in various angels or occluded with different parts of their sizes.


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  1. 1.
    Bowling, M.B., Pendell, D.L., Morris, D.L., Yoon, Y., Katoh, K., Belk, K.E., Smith, G.C.: Review: Identification and traceability of cattle in selected countries outside of north america. The Professional Animal Scientist 24(4), 287–294 (2008)Google Scholar
  2. 2.
    No, R.E.: 1760/2000 of the European Parliament and of the Council of 17 July 2000 establishing a system for the identification and registration of bovine animals and regarding the labelling of beef and beef products and repealing Council Regulation (EC) No 820/97 (2000), (Online; accessed in March 2014)
  3. 3.
    Velez, J., Sanchez, A., Sanchez, J., Esteban, J.: Beef identification in industrial slaughterhouses using machine vision techniques. Spanish Journal of Agricultural Research 11(4), 945–957 (2013)CrossRefGoogle Scholar
  4. 4.
    Schroeder, T.C., Tonsor, G.T.: International cattle id and traceability: Competitive implications for the us. Food Policy 37(1), 31–40 (2012)CrossRefGoogle Scholar
  5. 5.
    Marchant, J.: Secure animal identification and source verification. In: JM Communications. Copyright Optibrand Ltd., LLC (2002)Google Scholar
  6. 6.
    Shanahan, C., Kernan, B., Ayalew, G., McDonnell, K., Butler, F., Ward, S.: A framework for beef traceability from farm to slaughter using global standards: An irish perspective. Computers and Electronics in Agriculture 66(1), 62–69 (2009)CrossRefGoogle Scholar
  7. 7.
    Allen, A., Golden, B., Taylor, M., Patterson, D., Henriksen, D., Skuce, R.: Evaluation of retinal imaging technology for the biometric identification of bovine animals in northern ireland. Livestock Science 116(1), 42–52 (2008)CrossRefGoogle Scholar
  8. 8.
    Ahrendt, P., Gregersen, T., Karstoft, H.: Development of a real-time computer vision system for tracking loose-housed pigs. Computers and Electronics in Agriculture 76(2), 169–174 (2011)CrossRefGoogle Scholar
  9. 9.
    Corkery, G., Gonzales-Barron, U.A., Butler, F., McDonnell, K., Ward, S.: A preliminary investigation on face recognition as a biometric identifier of sheep (2007)Google Scholar
  10. 10.
    Baranov, A., Graml, R., Pirchner, F., Schmid, D.: Breed differences and intra-breed genetic variability of dermatoglyphic pattern of cattle. Journal of Animal Breeding and Genetics 110(1-6), 385–392 (1993)CrossRefGoogle Scholar
  11. 11.
    Minagawa, H., Fujimura, T., Ichiyanagi, M., Tanaka, K.: Identification of beef cattle by analyzing images of their muzzle patterns lifted on paper. Publications of the Japanese Society of Agricultural Informatics 8, 596–600 (2002)Google Scholar
  12. 12.
    Awad, A.I., Zawbaa, H.M., Mahmoud, H.A., Nabi, E.H.H.A., Fayed, R.H., Hassanien, A.E.: A robust cattle identification scheme using muzzle print images. In: 2013 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 529–534. IEEE (2013)Google Scholar
  13. 13.
    Choi, S.E., Lee, Y.J., Lee, S.J., Park, K.R., Kim, J.: Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recognition 44(6), 1262–1281 (2011)CrossRefMATHGoogle Scholar
  14. 14.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  15. 15.
    Scholkopft, B., Mullert, K.R.: Fisher discriminant analysis with kernels (1999)Google Scholar
  16. 16.
    Noviyanto, A., Arymurthy, A.M.: Automatic cattle identification based on muzzle photo using speed-up robust features approach. In: Proceedings of the 3rd European Conference of Computer Science, ECCS, pp. 110–114 (2012)Google Scholar
  17. 17.
    Noviyanto, A., Arymurthy, A.M.: Beef cattle identification based on muzzle pattern using a matching refinement technique in the sift method. Computers and Electronics in Agriculture 99, 77–84 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alaa Tharwat
    • 1
    • 6
  • Tarek Gaber
    • 2
    • 6
  • Aboul Ella Hassanien
    • 3
    • 6
  • Hasssan A. Hassanien
    • 4
    • 6
  • Mohamed F. Tolba
    • 5
  1. 1.Faculty of EngSuez Canal UniversityIsmailiaEgypt
  2. 2.Faculty of Computers and InformaticsSuez Canal UniversityIsmailiaEgypt
  3. 3.Faculty of Computers and InformationCairo UniversityCairoEgypt
  4. 4.Faculty of Veterinary MedicineCairo UniversityCairoEgypt
  5. 5.Faculty of Computers and InformationAin Shams UniversityCairoEgypt
  6. 6.Scientific Research Group in Egypt (SRGE)CairoEgypt

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