Abstract
Although human face recognition has achieved exceptional success driven by deep learning, animal face recognition (AFR) is still a research field that received less attention. Due to the big challenge in collecting large-scale animal face datasets, it is difficult to train a high-precision AFR model from scratch. In this work, we propose a novel Residual InterSpecies Equivariant Network (RiseNet) to deal with the animal face recognition task with limited training samples. First, we formulate a module called residual inter-species feature equivariant to make the feature distribution of animals face closer to the human. Second, according to the structural characteristic of animal face, the features of the upper and lower half faces are learned separately. We present an animal facial feature fusion module to treat the features of the lower half face as additional information, which improves the proposed RiseNet performance. Besides, an animal face alignment strategy is designed for the preprocessing of the proposed network, which further aligns with the human face image. Extensive experiments on two benchmarks show that our method is effective and outperforms the state-of-the-arts.
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Acknowledgements
This research was supported by grants from the National Natural Science Foundation of China (No. 61976219), the Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2016-AII) and the Fundamental Research Funds for Central Non-profit Scientific Institution (No. 2019JKY040). Portions of the research in this paper use the THoDBRL’2015 Database collected by the Research Groups in Intelligent Machines, University of Sfax, Tunisia.
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Shi, X., Yang, C., Xia, X., Chai, X. (2020). Deep Cross-Species Feature Learning for Animal Face Recognition via Residual Interspecies Equivariant Network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12372. Springer, Cham. https://doi.org/10.1007/978-3-030-58583-9_40
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