Image-based re-identification of animal individuals allows gathering of
information such as migration patterns of the animals over time. This, together
with large image volumes collected using camera traps and crowdsourcing, opens
novel possibilities to study animal populations. For many species, the
re-identification can be done by analyzing the permanent fur, feather, or skin
patterns that are unique to each individual. In this paper, we address the
re-identification by combining two types of pattern similarity metrics: 1)
pattern appearance similarity obtained by pattern feature aggregation and 2)
geometric pattern similarity obtained by analyzing the geometric consistency of
pattern similarities. The proposed combination allows to efficiently utilize
both the local and global pattern features, providing a general
re-identification approach that can be applied to a wide variety of different
pattern types. In the experimental part of the work, we demonstrate that the
method achieves promising re-identification accuracies for Saimaa ringed seals
and whale sharks.

图像基础下的动物个体再识别通过分析动物个体的固有毛皮、羽毛或皮肤图案来研究动物种群的时移模式，本文通过结合两种类型的图案相似度度量（图案特征聚合得到的图案外观相似度和分析图案相似性的几何图案相似度）提出了一种高效利用局部和全局图案特征的再识别方法，适用于多种不同类型的图案。实验部分表明，该方法在洞庭湖环封印和鲸鲨的再识别准确度上表现出了有希望的结果。