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Arabian Horse Identification System Based on Live Captured Muzzle Print Images

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017 (AISI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 639))

Abstract

The Arabian horse is one of the oldest and purebred breeds of horses in the entire world. The Arabian horse is characterized by speed, strength, and great beauty, full of quality, elegance, and dignity compared to other breeds. The Arabian horse identification is critical to controlling the disease outbreak, vaccination management, production management, and assigning ownership. In this paper, we represented Arabian horse identification system by using muzzle print images. The system has three processes; the first is the enrolment process which use Scale Invariant Feature Transform (SIFT) algorithm to extract the features of muzzle print images then store it in the database. The second process is matching process which matching the input muzzle print image with stored images in the database Random Sample Consensus (RANSAC) algorithm comes at the end of the matching process to remove any outlier, mismatched SIFT keypoints, and ensure the robustness of the similarity score. Finally, the Arabian horse identity is then assigned according to the highest estimated similarity score between the input image and the template one.

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References

  1. Vlad, M., Parvulet, R.A., Vlad, M.S.: A survey of livestock identification systems. In: 13th WSEAS International Conference on Automation and Information, (ICAI12). WSEAS Press, Iasi, pp. 165–170, June 2012

    Google Scholar 

  2. Klindtworth, M., Wendl, G., Klindtworth, K., Pirkelmann, H.: Electronic identification of cattle with injectable transponders. Comput. Electron. Agric. 24(12), 65–79 (1999)

    Article  Google Scholar 

  3. Roberts, C.: Radio frequency identification (RFID). Comput. Secur. 25(1), 18–26 (2006)

    Article  Google Scholar 

  4. El Hadad, H., Mahmoud, A., Mousa, F.A.: Bovines muzzle classification based on machine learning techniques. Proc. Comput. Sci. 65, 864–871 (2015)

    Article  Google Scholar 

  5. Petersen, W.: The identification of the bovine by means of nose-prints. J. Dairy Sci. 5(3), 249–258 (1922)

    Article  MathSciNet  Google Scholar 

  6. Baranov, A., Graml, R., Pirchner, F., Schmid, D.: Breed differences and intra-breed genetic variability dermatoglyphic pattern of cattle. J. Anim. Breed. Genet. 110(1–6), 385–392 (1993)

    Article  Google Scholar 

  7. Minagawa, H., Fujimura, T., Ichiyanagi, M., Tanaka, K.: Identification of beef cattle by analyzing images of their muzzle patterns lifted on paper. In: Third Asian Conference for Information Technology in Agriculture, (AFITA), Asian Agricultural Information Technology & Management, Beijing, China, pp. 596–600 (2002)

    Google Scholar 

  8. Awad, A.I., Hassanien, H.E., Zawbaa, H.M.: A Cattle Identification Approach Using Live Captured Muzzle Print Images. Adv. Secur. Inf. Commun. Netw. 381, 143–152 (2013)

    Article  Google Scholar 

  9. Noviyanto, A., Arymurthy, A.M.: Beef cattle identification based on muzzle pattern using a matching refinement technique in the sift method. Comput. Electron. Agric. 99(1), 77–84 (2013)

    Article  Google Scholar 

  10. Lowe, D.G.: Object recognition from local scale-invariant features. In: The 7th IEEE International Conference on Computer Vision, ICCV 1999, Kerkyra, Corfu, Greece, pp. 1150–1157 (1999)

    Google Scholar 

  11. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  12. Cheng, L., Li, M., Liu, Y., Cai, W., Chen, Y., Yang, K.: Remote sensing image matching by integrating affine invariant feature extraction and RANSAC. Comput. Electric. Eng. 38(4), 1023–1032 (2012)

    Article  Google Scholar 

  13. Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004)

    Article  Google Scholar 

  14. Jain, A.K., Ross, A.A., Nandakumar, K..: Introduction to Biometrics. 1st edn. Springer (2011)

    Google Scholar 

  15. Nigam, I., Vatsa, M., Singh, R.: Ocular Biometrics: a survey of modalities and fusion approaches. Inf. Fusion 26, 1–35 (2015)

    Article  Google Scholar 

  16. 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. Prof. Anim. Sci. 24(4), 287–294 (2008)

    Google Scholar 

  17. Shanahan, C., Kernan, B., Ayalew, G., McDonnell, K., Butler, F., Ward, S.: A Frame work for beef traceability from farm to slaughter using global standards: an Irish perspective. Comput. Electron. Agric. 66(1), 62–69 (2009)

    Article  Google Scholar 

  18. Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  19. Nejad, Z.H., Nasri, M.: An adaptive image registration method based on SIFT features and RANSAC transform. Computers and Electrical Engineering, pp. 1–14 (2016). http://www.vlfeat.org/last/accessed/2017/3/25

  20. Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms, http://www.vlfeat.org/last/accessed/2017/3/25

  21. Gaber, T., Tharwat, A., Hassanien, A.E., Snásel, V.: Biometric cattle identification approach based on weber’s local descriptor and AdaBoost classifier. Comput. Electron. Agric. 122, 55–66 (2016)

    Google Scholar 

  22. Tharwat, A., Gaber, T., Hassanien, A.E.: Two biometric approaches for cattle identification based on features and classifiers fusion. Int. J. Image Min. 1(4) (2015)

    Google Scholar 

  23. Taha, A., Darwish, A., Hassanien, A.E.: Arabian Horse Identification Benchmark Dataset, https://arxiv.org/abs/1706.04870

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Acknowledgment

We would like to thank the staff at EL-Zahraa Arabian Animal Farm in Egypt, for giving us the permission to collect the Arabian horse muzzles.

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Authors and Affiliations

  1. Faculty of Science, Al-Azhar University, Cairo, Egypt

    Ayat Taha

  2. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  3. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  4. Scientific Research Group in Egypt, Cairo, Egypt

    Ayat Taha, Ashraf Darwish & Aboul Ella Hassanien

Authors
  1. Ayat Taha
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  2. Ashraf Darwish
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  3. Aboul Ella Hassanien
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Corresponding author

Correspondence to Ayat Taha .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  3. Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

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Taha, A., Darwish, A., Hassanien, A.E. (2018). Arabian Horse Identification System Based on Live Captured Muzzle Print Images. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_73

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  • DOI: https://doi.org/10.1007/978-3-319-64861-3_73

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64860-6

  • Online ISBN: 978-3-319-64861-3

  • eBook Packages: EngineeringEngineering (R0)

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