Foundation Model Guidance for Tattoo Retrieval

Tattoos have been used effectively as soft biometrics to assist law enforcement in the identification of perpetrators and victims, as they have 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 exist. To overcome the handicap, this work presents a robust new tattoo retrieval framework that combines global features and geometrical precision extracted from foundation models for an accurate tattoo representation. The experimental evaluation conducted on a challenging tattoo database reported, in a closed-set protocol, a rank-1 accuracy of 90.91%, outperforming the state-of-the-art TattTRN by a large margin. Similar trends are also achieved for the open-set scenario: the Equal Error Rate is 11.56%, compared to 20.75% yielded by TattTRN. These results demonstrate that combining different foundation models without the need for training can significantly improve accuracy, setting a new benchmark for future tattoo recognition systems