[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2022.27.02.099

A Method of Dog Recognition using Nose Print and Landmarks

Kwak, Ho-Young (Dept. of Computer Engineering, Jeju National University)
Yun, Young-Min (Dept. of Veterinary Medicine, Jeju National University)
Chang, Jin-Wook (HRG Incorporated.)
Song, Woo Jin (Dept. of Veterinary Medicine, Jeju National University)
Kim, Soo Kyun (Dept. of Computer Engineering, Jeju National University)

Publication Information

Journal of the Korea Society of Computer and Information / v.27, no.2, 2022 , pp. 99-106 More about this Journal

Abstract

In this paper, We propose a method for identifying objects by setting inscriptions and landmarks of dogs. The phenomenon of abandoning dogs is on the rise, and the number of abandoned individuals is also rapidly increasing. These abandoned dogs are becoming wild animals, causing a lot of damage to people's daily life, causing serious problems. As a solution to this problem, the animal registration system is being implemented, but there is a phenomenon that some dog owners avoid the registration method that inserts a chip, so the complete registration system is not settled. When registering a dog, removing the avoidance of dog owners will help establish the companion animal registration system. In this paper, we present a technique to identify objects by setting inscriptions and landmarks of dogs so that dog owners can register their dogs in a friendly way to eliminate this avoidance phenomenon.

Keywords

Nose print; Landmark; Object recognition; Object identification; Registration system;

Citations & Related Records

Reference

1	M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol.24(6), pp.381-395, 1981. DOI
2	2020 Pet Food Market, Korea Agro-Fisheries & Food Trade Corporation
3	MBN News, https://m.mbn.co.kr/tv/552/1067655
4	https://www.richellusa.com/nose-print-dogs-identification
5	H. Wang, H. Su, P. Chen, R. Hou, Z. Zhang and W. Xie, "Learning deep features for giant panda gender classification using face images", Proc. IEEE Int. Conf. Comput. Vis. Workshops, pp. 279-285, 2019.
6	https://mokga.tistory.com/35
7	D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol.60, no.2, pp.91-110, 2004. DOI
8	S. Yang, L. Bo, J. Wang, and L. G. Shapiro. Unsupervised template learning for fine-grained object recognition. In NIPS, pages 3131-3139, 2012.
9	2020 Companion Animal Protection and Welfare Survey Results, https://eiec.kdi.re.kr/policy/materialView.do?num=213939&topic=L&pp=20&datecount=&recommend=&pg=
10	Animal Welfare Awareness, Research and Education, "A public perception survey on the direction of improvement of animal welfare policies in 2021", Survey Report, Sep. 2021.
11	Finding Roverhttps://brookfieldvets.net/blog/166129-finding-rover-pet-facial-recognition-app
12	MEGVII, https://en.megvii.com/
13	H. B. Bae, D. Pak and S. Lee, "Dog Nose-Print Identification Using Deep Neural Networks," in IEEE Access, vol. 9, pp. 49141-49153, 2021, doi: 10.1109/ACCESS.2021.3068517. DOI
14	A. Alahi, R. Ortiz, and P. Vandergheynst, "Freak: Fast retina keypoint," Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on IEEE, pp.510-517, 2012.
15	S. Kumar, S. K. Singh, R. S. Singh, A. K. Singh and S. Tiwari, "Real-time recognition of cattle using animal biometrics", J. Real-Time Image Process., vol. 13, no. 3, pp. 505-526, Sep. 2017. DOI
16	K. Lai, X. Tu, S. Yanushkevich, "Dog Identification using Soft Biometrics and Neural Networks", 2019 IJCNN, pp.1-8, 2019. DOI: 10.1109/IJCNN.2019.8851971. DOI
17	M. Lee, J. Park, J. Jeong, "An improved system of Dog Identification based on Muzzle Pattern", KIBME Conference 2015, pp.199-202, 2015.
18	https://www.psychologytoday.com/us/blog/canine-corner/202109/are-dogs-nose-prints-really-unique-and-unchanging