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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References
Chollet, F.: How convolutional neural networks see the world. The Keras Blog 30 (2016)
De Souza, G.B., da Silva Santos, D.F., Pires, R.G., Marana, A.N., Papa, J.P.: Deep texture features for robust face spoofing detection. IEEE Trans. Circuits Syst. II Express Briefs 64(12), 1397–1401 (2017)
Deng, J., Guo, J., Liu, T., Gong, M., Zafeiriou, S.: Sub-center ArcFace: boosting face recognition by large-scale noisy web faces. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 741–757. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_43
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: Additive angular margin loss for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)
Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Forsyth, D.A., et al.: Object recognition with gradient-based learning. Shape, contour and grouping in computer vision, pp. 319–345 (1999)
GeeksforGeeks: Residual networks (resnet) - deep learning. https://www.geeksforgeeks.org/residual-networks-resnet-deep-learning/. Accessed 18 June 2022
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
Institute, P.B.: Pet census (2019). https://institutopetbrasil.com/imprensa/censo-pet-1393-milhoes-de-animais-de-estimacao-no-brasil. Accessed 18 June 2022
Jang, D.H., Kwon, K.S., Kim, J.K., Yang, K.Y., Kim, J.B.: Dog identification method based on muzzle pattern image. Appl. Sci. 10(24), 8994 (2020)
Kumar, S., Singh, S.K.: Visual animal biometrics: survey. IET. Biometrics 6(3), 139–156 (2017)
Lai, K., Tu, X., Yanushkevich, S.: Dog identification using soft biometrics and neural networks. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2019)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Lemos, S.: The number of adoptions and abandonment of animals in the pandemic (2021). https://jornal.usp.br/atualidades/cresce-o-numero-de-adocoes-e-de-abandono-de-animais-na-pandemia. Accessed 18 June 2022
Li, S., Jiao, J., Han, Y., Weissman, T.: Demystifying resnet. arXiv preprint arXiv:1611.01186 (2016)
Li, Y., Yuan, G., Wen, Y., Hu, J., Evangelidis, G., Tulyakov, S., Wang, Y., Ren, J.: Efficientformer: vision transformers at mobilenet speed. Adv. Neural. Inf. Process. Syst. 35, 12934–12949 (2022)
Mougeot, G., Li, D., Jia, S.: A deep learning approach for dog face verification and recognition. In: Nayak, A.C., Sharma, A. (eds.) PRICAI 2019. LNCS (LNAI), vol. 11672, pp. 418–430. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29894-4_34
Software, A.: Pet insurance fraud increases (2018). https://youtalk-insurance.com/broker-news/400-rise-in-pet-insurance-fraud-highlights-need-for-new-approach. Accessed 18 June 2022
Targ, S., Almeida, D., Lyman, K.: Resnet in resnet: Generalizing residual architectures (2016). arXiv preprint arXiv:1603.08029
Vaswani, A., et al.: Attention is all you need. Advances in neural information processing systems 30 (2017)
Yoon, B., So, H., Rhee, J.: A methodology for utilizing vector space to improve the performance of a dog face identification model. Appl. Sci. 11(5), 2074 (2021)
Zhang, K., Sun, M., Han, T.X., Yuan, X., Guo, L., Liu, T.: Residual networks of residual networks: multilevel residual networks. IEEE Trans. Circuits Syst. Video Technol. 28(6), 1303–1314 (2017)
Zhang, X., Zhao, R., Qiao, Y., Wang, X., Li, H.: Adacos: adaptively scaling cosine logits for effectively learning deep face representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10823–10832 (2019)
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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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