文献类型： 会议论文

第一作者： Xiao Shi

作者： Xiao Shi ¹ ; Chenxue Yang ¹ ; Xue Xia ¹ ; Xiujuan Chai ¹ ;

作者机构： 1.Agricultural Information Institute of CAAS

关键词： Animal face recognition;Interspecies;Fine-tuning;Feature equivariant;Feature fusion

会议名称： European Conference on Computer Vision

主办单位：

页码： 667-682

摘要： 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.

分类号： TP391.41-53