Abstract
Visual features associated with cattle and pigs cannot just be used for individual biometric identification, they can also be used to organize the domain into distinct breeds. In this paper, we use cropped muzzle images of pigs for breed identification. Gradient patch density maps are first created, and then the patch density profile distribution tailored to a specific breed is learnt to characterize the feature space for each of the four pig breeds: Duroc, Ghungroo, Hampshire, and Yorkshire. A Maximal Likelihood (ML) inferencing at the patch level followed by a second-tier decision fusion based on majority vote has been used to detect the breed from any query feature computed from an arbitrary muzzle image. Duroc, Ghungroo, and Yorkshire show good classification accuracies of 75.86%, 70.59%, and 100%, respectively, while the accuracy drops for Hampshire to 58.78% on account of the intrinsic white patch diversity in the breed and its similarity to Duroc and Ghungroo on some counts.
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References
Cai, C., Li, J.: Cattle face recognition using local binary pattern descriptor. In: Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, Asia-Pacific, pp. 1–4 (2013)
Awad, A.I., Zawbaa, H.M., Mahmoud, H.A., Abdel Nabi, E.H.H., Fayed, R.H., Hassanien, A.E.: A robust cattle identification scheme using muzzle print images. In: 2013 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, pp. 529–534 (2013)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Shi, G., Xu, X., Dai, Y.: Sift feature point matching based on improved RANSAC algorithm. In: Proceedings of the 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics—Volume 01, IHMSC’13. IEEE Computer Society, Washington, DC, USA, pp. 474–477 (2013)
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, vol. 110, p. 114 (2012)
Borah, S., Samajdar, T., Das, T.K., Marak, G.R.: Success story on income generation from pig: Ghungroo. http://www.kiran.nic.in/ss_income_generation_pig_ghungroo.html
National Swine Registry.: Yorkshire. http://nationalswine.com/about/about_breeds/yorkshire.php
WIKIPEDIA.: Duroc Pig. https://en.wikipedia.org/wiki/duroc_pig
National Swine Registry.: Hampshire. http://nationalswine.com/about/about_breeds/hampshire.php
Raymond, J., Scientific American.: The Shape of a Nose (2011). https://www.scientificamerican.com/article/the-shape-of-a-nose/
Acknowledgements
This work is a byproduct of an IMAGE IDGP subproject, under the purview of a broader initiative, related to Identification, Tracking, and Epidemiological analysis of pigs and goats. This comes under the ITRA frame, which is a national-level research initiative controlled and sponsored by the Ministry of Information Technology, Government of India. The authors thank Dr. Santanu Banik and his ICAR RANI team (Rani, Guwahati, and Assam) for collaborating with us [the signal processing partners from IIT Guwahati] and for capturing samples of Annotated Muzzle Images from the ICAR Rani pig farm, which eventually formed the repository for the breed analysis.
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Chakraborty, S., Karthik, K., Banik, S. (2020). Investigation on the Muzzle of a Pig as a Biometric for Breed Identification. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_7
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DOI: https://doi.org/10.1007/978-981-32-9088-4_7
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