Publication: Biometric for Cattle Identification using Muzzle Patterns
Issued Date
2020-01-01
Resource Type
File Type
video/youtube
ISSN
02180014
Other identifier(s)
2-s2.0-85082416936
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal of Pattern Recognition and Artificial Intelligence. (2020)
Suggested Citation
Worapan Kusakunniran, Anuwat Wiratsudakul, Udom Chuachan, Sarattha Kanchanapreechakorn, Thanandon Imaromkul, Noppanut Suksriupatham, Kittikhun Thongkanchorn Biometric for Cattle Identification using Muzzle Patterns. International Journal of Pattern Recognition and Artificial Intelligence. (2020). doi:10.1142/S0218001420560078 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/54527
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Biometric for Cattle Identification using Muzzle Patterns
Other Contributor(s)
Abstract
© 2020 World Scientific Publishing Company. Similar to human biometrics such as faces and fingerprints, animals also have biometrics for individual identifiers. This research paper works on biometrics of cattle using images of muzzle patterns. The proposed approach begins with a training process to construct a cattle face localization model using a Haar feature-based cascade classifier. Then, the watershed technique is applied to segment a region of interest (RoI) of a muzzle area in the detected region of the cattle face. This muzzle ROI is further enhanced to make ridge lines more outstanding. The next step, using two approaches, is to extract a main feature descriptor based on a bag of histograms of oriented gradients (BoHoG) and a histogram of local binary patterns (LBP). Then, the support vector machine (SVM) is applied with the histogram intersection kernel for a final cattle identifier. The proposed method is evaluated using five different datasets including one existing cattle dataset used in previous research works, one newly collected dataset of swamp buffalo captured in a controlled environment, and three newly collected datasets of swamp buffalo captured in an outdoor field environment. This outdoor field environment includes challenges of freely moving cattle and differences in daylight. It could achieve a promising accuracy of 95% for a large dataset of 431 subjects.
