TattTRN: Template Reconstruction Network for Tattoo Retrieval

Lazaro Janier Gonzalez-Soler, Maciej Salwowski, Christian Rathgeb, Daniel Fischer; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 1529-1538

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 28550 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.