Cattle Identi cation using Muzzle Print Images based on Texture Features Approach

Alaa Tharwat, T. Gaber, A. E. Hassanien, H. A. Hassanien, and M. F. Tolba, "Cattle Identi cation using Muzzle Print Images based on Texture Features Approach", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014.

Date Presented:

22-24 June


The increasing growth of the world trade and growing con-
cerns of food safety by consumers need a cutting-edge animal identi-
cation and traceability systems as the simple recording and reading
of tags-based systems are only eective in eradication programs of na-
tional disease. Animal biometric-based solutions, e.g. muzzle imaging
system, oer an eective and secure, and rapid method of addressing
the requirements of animal identication and traceability systems. In
this paper, we propose a robust and fast cattle identication approach.
This approach makes use of Local Binary Pattern (LBP) to extract local
invariant features from muzzle print images. We also applied dierent
classiers including Nearest Neighbor, Naive Bayes, SVM and KNN for
cattle identication. The experimental results showed that our approach
is superior than existed works as ours achieves 99,5% identication accu-
racy. 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 dierent parts of their sizes.

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