Review of the Current Research in Animal Individual Recognition

Authors

  • Kailuo Lin

DOI:

https://doi.org/10.61173/4wat1n55

Keywords:

animal, identification, facial recognition, iris recognition, radio frequency identification (RFID), convolutional neural networks (CNNs)

Abstract

With the development of biological identification for human beings, it also develops such as fingerprint, facial recognition, and iris identification. Scientists and zoologists must be more content with the cumbersome ways of identifying previously existing animals. Old methods such as ear cutting, chip implantation, and nose print recognition harm animals and humans. It has to catch the animals or needed professionals for individual identification (e.g, identify sharks by taking photos of their fins). Therefore, more and more scientists and engineers are trying to develop more efficient methods for individual animal identification. Of course, there are also many very constructive cases. Thus, this literature review will provide information about three regions of the latest recognition technology: radio-frequency identification (RFID), animal facial identification, and iris recognition.

References

[1] Akhilesh Kumar Singh, Sudipta Ghosh, et.al (2014) Application of radio frequency identification (RFID) technology in dairy herd management. http://ijlr.org/issue/applicationradio-frequency-identification-rfid-technology-dairy-herdmanagement/

[2] J. Gomes, M. Shimizu, et.al (2019) Use of UHF (Ultra High Frequency) RFID technology in the data capture, traceability and monitoring interface in the official cattle animal identification program. https://www.cabdirect.org/cabdirect/ abstract/20203197447

[3] Felix Adrion, M. Keller, C. Umstatter, et.al (2020) Setup, Test and Validation of a UHF RFID System for Monitoring Feeding Behaviour of Dairy Cows. https://www.mdpi.com/1424- 8220/20/24/7035

[4] Nippon Electric Company (2022) A Brief History of Facial Recognition. www.nec.co.nz/market-leadership /publicationsmedia/a-brief-history-of-facial-recognition/..

[5] Gu, Jiuxiang, et al. (2018)”Recent advances in convolutional neural networks.” https://arxiv.org/pdf/1512.07108.pdf

[6] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[7] Daniel Schofield, et al.(2019) Chimpanzee Face Recognition from Videos in the Wild Using Deep Learning. https://www. science.org/doi/10.1126/sciadv.aaw0736

[8] Clapham, Melanie, et al. (2020) Automated facial recognition for wildlife that lack unique markings: A deep learning approach for brown bears. https://onlinelibrary. wiley.com/doi/10.1002/ece3.6840

[9] Hansen, Mark F., et al. (2018) Towards on-farm pig face recognition using convolutional neural networks. https://www. sciencedirect.com/science/article/pii/S0166361517304992

[10] Parthasarathi De and Dibyendu Ghoshal. (2016) Recognition of Non Circular Iris Pattern of the Goat by Structural, Statistical and Fourier Descriptors https://www. sciencedirect.com/science/article/pii/S1877050916311358

[11] X.Wang, L. Zhao and Q.Kong. (2009) Iris Recognition System Design and Development of Large Animals for Tracing Source of Infection. https://www.sciencedirect.com/science/ article/pii/S1877050916311358

[12] Mateusz Trokielewicz, M. Szadkow, et.al (2017) Iris and periocular recognition in arabian race horses using deep convolutional neural networks. https://ieeexplore.ieee.org/ document/8272736

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Published

2023-06-01