Abstract
The quality of animal identification system plays an important role for producers to make management decisions about their herd or individual animals. The animal identification is also important to animal traceability systems to ensure the integrity of the food chain. Usually, recordings and readings of tags-based systems are used to identify an animal, but only effective in eradication programs of national disease. Recently, 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 present an identification system based on muzzle images. The identification process is based on Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Tucker Tensor Decomposition. This selected classifiers we compared on the same dataset of muzzle images with different experiment settings. The results we evaluated by F-score. The best F-score result gives us the Tucker Tensor Decomposition. It achieved the median of F-score 0.750.
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Acknowledgments
This work was supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme and by Project SP2015/105 “DPDM - Database of Performance and Dependability Models” of the Student Grand System, VŠB - Technical University of Ostrava and by Project SP2015/146 “Parallel processing of Big data 2” of the Student Grand System, VŠB - Technical University of Ostrava.
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Zaorálek, L., Prilepok, M., Snášel, V. (2016). Cattle Identification Using Muzzle Images. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. Advances in Intelligent Systems and Computing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-29504-6_11
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DOI: https://doi.org/10.1007/978-3-319-29504-6_11
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