Abstract
Over 470 million dogs are kept as pets around the world. Dogs are owned at an average number of \(1.6\%\) per household. The US has the most dog pets, where about \(68\%\) of households own at least one pet. Lost and missing dogs are a severe source of suffering and problems for their families. So, this paper addresses the problem of facial dog identification. This technology can benefit many applications, such as handling the missing pet problem, granting pets access to their houses, more intelligent zoonosis control, pet health care, and tracking stray pets. We evaluate a Residual Convolutional Neural Network, specifically ResNet-34, for facial identification in dogs. We tested in DogFaceNet and Flickr-dog datasets with and without two face preprocessing techniques: a central crop and an aligned facial extraction. Experimental results show promising results surpassing the state-of-the-art: \(97.6\%\) and \(82.8\%\) accuracies for DogFaceNet and Flickr-dog, respectively. Moreover, we also provide recall metrics for the best models.
Supported by organization FUNCAP.
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Acknowledgements
The authors would like to thank The Ceará State Foundation for the Support of Scientific and Technological Development (FUNCAP) for the financial support (6945087/2019).
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Andrade, J.P.B., Costa, L.F., Fernandes, L.S., Rego, P.A.L., Maia, J.G.R. (2023). Dog Face Recognition Using Deep Features Embeddings. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14196. Springer, Cham. https://doi.org/10.1007/978-3-031-45389-2_9
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