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JBE, vol. 27, no. 5, pp.794-807, September, 2022
A Study on Improving Facial Recognition Performance to Introduce a New Dog Registration Method
Dongsu Lee and Gooman Park
C.A E-mail: firstname.lastname@example.org
Although registration of dogs is mandatory according to the revision of the Animal Protection Act, the registration rate is low due to the inconvenience of the current registration method. In this paper, a performance improvement study was conducted on the dog face recognition technology, which is being reviewed as a new registration method. Through deep learning learning, an embedding vector for facial recognition of a dog was created and a method for identifying each dog individual was experimented. We built a dog image dataset for deep learning learning and experimented with InceptionNet and ResNet-50 as backbone networks. It was learned by the triplet loss method, and the experiments were divided into face verification and face recognition. In the ResNet-50-based model, it was possible to obtain the best facial verification performance of 93.46%, and in the face recognition test, the highest performance of 91.44% was obtained in rank-5, respectively. The experimental methods and results presented in this paper can be used in various fields, such as checking whether a dog is registered or not, and checking an object at a dog access facility.
Keyword: Deep Learning, Face Recognition, Dog Recognition, ResNet, Triplet Loss
 D. Lee and G. Park, "Analysis of Transfer Learning Effect for Automatic Dog Breed Classification," Journal of Broadcasting Engineering, Vol. 27, No. 1, pp.133-145, Jan. 2022. doi : https://doi.org/10.5909/JBE.2022.27.1.133
 Liu, A. Kanazawa, D. Jacobs, and P. Belhumeur, "Dog Breed Classification using Part Localization," European Conference on Computer Vision(ECCV), pp.172–185, 2012. doi : https://doi.org/10.1007/978-3-642-33718-5_13
 X. Wang, V. Ly, S. Sorensen, and C. Kambhamettu, “Dog breed classification via landmarks,” International Conference on Image Procesing(ICIP) 2014, pp. 5237–5241, January 2015. doi : https://doi.org/10.1109/ICIP.2014.7026060
 G. Mougeot, D. Li, and S Jia, "A Deep Learning Approach for Dog Face Verification and Recognition," PRICAI 2019: Trends in Artificial Intelligence, pp. 418–430, 2019. doi : https://doi.org/10.1007/978-3-030-29894-4_34
 S. Kumar and S. K. Singh, “Monitoring of pet animal in smart cities using animal biometrics,” Future Generation Computer Systems, Vol. 83, pp. 553–563, 2018.
 The DogFaceNet Dataset 1, https://github.com/GuillaumeMougeot/ DogFaceNet/releases (accessed Mar. 02, 2022) doi : https://doi.org/10.1016/j.future.2016.12.006
 D. G. Lowe, “Distinctive Image Features from ScaleInvariant Keypoints,” International Journal of Computer Vision, vol.60, issue.2, pp.91~110, 2004. doi : https://doi.org/10.1023/B:VISI.0000029664.99615.94
 H. Bay, T. Tuytelaars, and L. Gool, “SURF: Speeded Up Robust Features,” Computer Vision and Image Understanding, Vol.110, No.3, pp.346~359, 2008. doi : https://doi.org/10.1007/11744023_32
 A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, Vol. 2, pp.1097–1105. 2012. doi : https://doi.org/10.1145/3065386
 Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the Gap to Human-level Performance in Face Verification,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708, 2014.
 F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A Unified Embedding for Face Recognition and Clustering,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823, 2015.
 M. D. Zeiler and R. Fergus. "Visualizing and Understanding Convolutional Networks," European Conference on Computer Vision(ECCV), pp.818-833, 2014. doi : https://doi.org/10.1007/978-3-319-10590-1_53
 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. "Going Deeper with Convolutions," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1-9, 2015.
 Y. Sun, X. Wang, and X. Tang. "Deeply Learned Face Representations are Sparse, Selective, and Robust," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2892-2900, 2015.
 W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song. "SphereFace: Deep Hypersphere Embedding for Face Recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.212-220, 2017.
 H. Wang, Y. Wang, Z. Zhou, X. Ji, Z. Li, D. Gong, J. Zhou, and W. Liu, “Cosface: Large margin cosine loss for deep face recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salk Lake City, US, pp. 5265-5274, 2018.
 M. Lee, J. Park, and J. Jeong, "An improved system of Dog Identification based on Muzzle Pattern," Proceedings of the Korean Society of Broadcast and Media Engineers Conference, pp.199-202, 2015.
 J. Liu, A. Kanazawa, D. Jacobs, and P. Belhumeur, "Dog Breed Classification using Part Localization," European Conference on Computer Vision(ECCV), pp.172–185, 2012. doi : https://doi.org/10.1007/978-3-642-33718-5_13
 Adam Klein. Pet Cat Face Verification and Identification, Stanford University, CS230 Fall 2019.