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.
Keywords
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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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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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