A Cattle Identification Approach Using Live Captured Muzzle Print Images

  • Ali Ismail Awad
  • Aboul Ella Hassanien
  • Hossam M. Zawbaa
Part of the Communications in Computer and Information Science book series (CCIS, volume 381)


Cattle identification receives a great research attention as a dominant way to maintain the livestock. The identification accuracy and the processing time are two key challenges of any cattle identification methodology. This paper presents a robust and fast cattle identification approach from live captured muzzle print images with local invariant features. The presented approach compensates some weakness of traditional cattle identification schemes in terms of accuracy and processing time. The proposed scheme uses Scale Invariant Feature Transform (SIFT) for detecting the interesting points for image matching. In order to enhance the robustness of the presented technique, a Random Sample Consensus (RANSAC) algorithm has been coupled with the SIFT output to remove the outlier points and achieve more robustness. The experimental evaluations prove the superiority of the presented approach because it achieves 93.3% identification accuracy in reasonable processing time compared to 90% identification accuracy achieved by some other reported approaches.


Similarity Score Scale Invariant Feature Transform Biometric Trait Scale Invariant Feature Transform Feature Biometric Technology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Vlad, M., Parvulet, R.A., Vlad, M.S.: A survey of livestock identification systems. In: Proceedings of the 13th WSEAS International Conference on Automation and Information, ICAI 2012, pp. 165–170. WSEAS Press, Iasi (2012)Google Scholar
  2. 2.
    Roberts, C.: Radio frequency identification (RFID). Computers & Security 25(1), 18–26 (2006)CrossRefGoogle Scholar
  3. 3.
    Jain, A.K., Ross, A.A., Nandakumar, K.: Introduction to Biometrics. Springer (2011)Google Scholar
  4. 4.
    Giot, R., El-Abed, M., Rosenberger, C.: Fast computation of the performance evaluation of biometric systems: Application to multibiometrics. Future Generation Computer Systems 29(3), 788–799 (2013), Special Section: Recent Developments in High Performance Computing and SecurityGoogle Scholar
  5. 5.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 4–20 (2004)CrossRefGoogle Scholar
  6. 6.
    Egawa, S., Awad, A.I., Baba, K.: Evaluation of acceleration algorithm for biometric identification. In: Benlamri, R. (ed.) NDT 2012, Part II. CCIS, vol. 294, pp. 231–242. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Petersen, W.: The identification of the bovine by means of nose-prints. Journal of Dairy Science 5(3), 249–258 (1922)CrossRefGoogle Scholar
  8. 8.
    Minagawa, H., Fujimura, T., Ichiyanagi, M., Tanaka, K.: Identification of beef cattle by analyzing images of their muzzle patterns lifted on paper. In: Proceedings of the Third Asian Conference for Information Technology in Agriculture, AFITA 2002: Asian Agricultural Information Technology & Management, Beijing, China, pp. 596–600 (October 2002)Google Scholar
  9. 9.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision 3(3), 177–280 (2008)CrossRefGoogle Scholar
  11. 11.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of 7th IEEE International Conference on Computer Vision, ICCV 1999, Kerkyra, Corfu, Greece, pp. 1150–1157 (September 1999)Google Scholar
  12. 12.
    Iannizzotto, G., Rosa, F.L.: A SIFT-based fingerprint verification system using cellular neural networks. In: Pattern Recognition Techniques, Technology and Applications, pp. 523–536. InTech (2008)Google Scholar
  13. 13.
    Park, U., Pankanti, S., Jain, A.K.: Fingerprint verification using SIFT features. In: Proceedings of SPIE Defense and Security Symposium (2008)Google Scholar
  14. 14.
    Awad, A.I., Baba, K.: Evaluation of a fingerprint identification algorithm with SIFT features. In: Proceedings of the 3rd 2012 IIAI International Conference on Advanced Applied Informatics, pp. 129–132. IEEE, Fukuoka (2012)Google Scholar
  15. 15.
    Chen, J., Moon, Y.S.: Using SIFT features in palmprint authentication. In: Procedings of 19th International Conference on Pattern Recognition, pp. 1–4. IEEE (2008)Google Scholar
  16. 16.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Jain, A.K., Bolle, R., Pankanti, S.: Biometrics: Personal Identification in Networked Society, 2nd edn. Springer (2005)Google Scholar
  18. 18.
    Jain, A., Ross, A., Pankanti, S.: Biometrics: a tool for information security. IEEE Transactions on Information Forensics and Security 1(2), 125–143 (2006)CrossRefGoogle Scholar
  19. 19.
    Ratha, N.K., Connell, J.H., Bolle, R.M.: Enhancing security and privacy in biometrics-based authentication systems. IBM Systems Journal 40(3), 614–634 (2001)CrossRefGoogle Scholar
  20. 20.
    Lee, Y., Filliben, J.J., Micheals, R.J., Phillips, P.J.: Sensitivity analysis for biometric systems: A methodology based on orthogonal experiment designs. Computer Vision and Image Understanding p. (in press, 2013)Google Scholar
  21. 21.
    Schouten, B., Jacobs, B.: Biometrics and their use in e-passports. Image and Vision Computing 27(3), 305–312 (2009), special Issue on Multimodal BiometricsGoogle Scholar
  22. 22.
    Awad, A.I.: Machine learning techniques for fingerprint identification: A short review. In: Hassanien, A.E., Salem, A.-B.M., Ramadan, R., Kim, T.-H. (eds.) AMLTA 2012. CCIS, vol. 322, pp. 524–531. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  23. 23.
    International Biometric Group: Biometrics market and industry report 2009-2014 (March 2008),
  24. 24.
    Li, Y.: Biometric technology overview. Nuclear Science and Techniques 17(2), 97–105 (2006)CrossRefGoogle Scholar
  25. 25.
    Luis-Garcia, R.D., Alberola-Lopez, C., Aghzout, O., Ruiz-Alzola, J.: Biometric identification systems. Signal Processing 83(12), 2539–2557 (2003)zbMATHCrossRefGoogle Scholar
  26. 26.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, 2nd edn. Springer (2009)Google Scholar
  27. 27.
    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 2012, pp. 110–114. WSEAS Press, Paris (2012)Google Scholar
  28. 28.
    Cheng, L., Li, M., Liu, Y., Cai, W., Chen, Y., Yang, K.: Remote sensing image matching by integrating affine invariant feature extraction and RANSAC. Computers & Electrical Engineering 38(4), 1023–1032 (2012)CrossRefGoogle Scholar
  29. 29.
    Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008),

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ali Ismail Awad
    • 1
  • Aboul Ella Hassanien
    • 2
  • Hossam M. Zawbaa
    • 1
  1. 1.Faculty of EngineeringAl Azhar UniversityQenaEgypt
  2. 2.Faculty of Computers & InformationCairo UniversityCairoEgypt

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