Abstract
The demand for effective, efficient and safe methods for animal identification has been increasing significantly, due to the need for traceability, management, and control of this population, which grows at higher rates than the human population, particularly pets. Motivated by the efficacy of modern human identification methods based on face biometrics features, in this paper, we propose a dog face recognition method based on vision transformers, a deep learning approach that decomposes the input image into a sequence of patches and applies self-attention to these patches to capture spatial relationships between them. Results obtained on DogFaceNet, a public database of dog face images, show that the proposed method, which uses the EfficientFormer-L1 architecture, outperforms the state-of-the-art method proposed previously in literature based on ResNet, a deep convolutional neural network.
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Canto, V.H.B., Manesco, J.R.R., de Souza, G.B., Marana, A.N. (2023). Dog Face Recognition Using Vision Transformer. 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_3
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