Tattoo-ID: Automatic Tattoo Image Retrieval for Suspect and Victim Identification

Tattoos are used by law enforcement agencies for identification of a victim or a suspect using a false identity. Current method for matching tattoos is based on human-assigned class labels that is time consuming, subjective and has limited performance. It is desirable to build a content-based image retrieval (CBIR) system for automatic matching and retrieval of tattoos. We examine several key design issues related to building a prototype CBIR system for tattoo image database. Our system computes the similarity between the query and stored tattoos based on image content to retrieve the most similar tattoos. The performance of the system is evaluated on a database of 2,157 tattoos representing 20 different classes. Effects of segmentation errors, image transformations (e.g., blurring, illumination), influence of semantic labels and relevance feedback are also studied.