Tattoos have been used effectively as soft biometrics to assist law
enforcement in the identification of offenders and victims, as they contain
discriminative information, and are a useful indicator to locate members of a
criminal gang or organisation. Due to various privacy issues in the acquisition
of images containing tattoos, only a limited number of databases exists. This
lack of databases has delayed the development of new methods to effectively
retrieve a potential suspect's tattoo images from a candidate gallery. To
mitigate this issue, in our work, we use an unsupervised generative approach to
create a balanced database consisting of 28,550 semi-synthetic images with
tattooed subjects from 571 tattoo categories. Further, we introduce a novel
Tattoo Template Reconstruction Network (TattTRN), which learns to map the input
tattoo sample to its respective tattoo template to enhance the distinguishing
attributes of the final feature embedding. Experimental results with real data,
i.e., WebTattoo and BIVTatt databases, demonstrate the soundness of the
presented approach: an accuracy of up to 99% is achieved for checking at most
the first 20 entries of the candidate list.

我们使用无监督生成方法创建了一个平衡的数据库，并引入了一种新颖的纹身模板重建网络 (TattTRN)，该网络通过学习将输入的纹身样本映射到其相应的纹身模板，以增强最终特征嵌入的区别性属性。实验证明了此方法的可行性，对于最多检查候选列表的前 20 个条目，可以达到高达 99% 的准确率。