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.

翻译：纹身因其包含区分性信息并可作为识别犯罪团伙或组织成员的有效指标，已被广泛用作软生物特征以协助执法部门识别罪犯与受害者。由于采集含纹身图像涉及诸多隐私问题，目前仅有少量数据库可供使用。数据库的匮乏阻碍了从候选库中有效检索潜在嫌疑人纹身图像的新方法发展。为解决该问题，本研究采用无监督生成方法构建了一个包含28,550张半合成图像的平衡数据库，这些图像来自571个纹身类别，且均带有纹身主体。此外，我们提出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纹身样本映射至对应纹身模板，从而增强最终特征嵌入的区分属性。基于真实数据（即WebTattoo和BIVTatt数据库）的实验结果表明了该方法的有效性：在检索候选列表前20个条目时，准确率最高可达99%。