Title: Analysing muzzle pattern images as a biometric for cattle identification

Authors: Worapan Kusakunniran; Anuwat Wiratsudakul; Udom Chuachan; Thanandon Imaromkul; Sarattha Kanchanapreechakorn; Noppanut Suksriupatham; Kittikhun Thongkanchorn

Addresses: Faculty of Information and Communication Technology, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom 73170, Thailand ' Department of Clinical Sciences and Public Health, Faculty of Veterinary Science, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom 73170, Thailand ' Veterinary Research and Development Center (Lower Northeastern Region), Mueang, Surin 32000, Thailand ' Veterinary Research and Development Center (Lower Northeastern Region), Mueang, Surin 32000, Thailand ' Faculty of Information and Communication Technology, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom 73170, Thailand ' Faculty of Information and Communication Technology, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom 73170, Thailand ' Faculty of Information and Communication Technology, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom 73170, Thailand

Abstract: Identifying individual animals is important for many reasons of population control, illegal trade prevention, and disease surveillance. This paper focuses on the cattle identification, using biometric-based solution of muzzle images. The proposed method begins with localising muzzle region in each image using the Haar-cascade-based classifier. The scale-invariant feature transform (SIFT) is applied to extract key points of muzzle patterns. Then, SIFT points are split into different clusters/types of muzzle patterns, called bags of muzzle-words (BoM). Finally, the support vector machine (SVM) model is built on BoM as the cattle identifier. The proposed method is evaluated on the published muzzle images dataset of cattle and the collected muzzle image dataset of slaughterhouses and preserved muzzles of swamp buffalo. This article reports the perfect accuracy of 100%. It is also evaluated with the collected dataset of muzzle images of swamp buffalo in the real fields with the reported accuracy of above 90%.

Keywords: cattle identification; muzzle images; animal biometric.

DOI: 10.1504/IJBM.2021.117852

International Journal of Biometrics, 2021 Vol.13 No.4, pp.367 - 384

Received: 19 Mar 2020
Accepted: 30 Jun 2020

Published online: 04 Oct 2021 *

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