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COMPETITIONS

Robust Tattoo Detection and Retrieval Competition (RTDRC 2019)

Organizers: Prof. Shiguang Shan, A/Prof. Hu Han, Dr Abhijit Das, Dr Antitza Dantcheva

Despite the enormous progress in biometrics-based primary modalities such as the face, iris and fingerprint, unimodal biometrics identification has not been accepted in forensics [5, 6]. Tattoos, which constitute a pertinent and highly distinctive soft biometric trait, can be particularly useful in describing wanted or missing people, or even unidentified bodies. Hence, tattoos are highly instrumental in person identification. Consequently, research on tattoo-based biometrics has gained significant interest in the last few years. In order to explore the potential of tattoos, various research directions have been proposed in the literature. However, existing tattoo search methods or tattoo retrieval techniques mainly focus on the matching of cropped tattoos. Therefore, these topics require more analysis. Open research problems include tattoo detection, as well as localization, i.e., determining whether an image contains a tattoo and if so, segmentation of the tattoo.

In addition, Sketch-Based Tattoo Search can be instrumental in many scenarios, e.g., if the surveillance image of a crime scene is not available, and the query is a tattoo sketch, drawn based a witness description. Therefore, it is important to evaluate the matching performance in similar scenarios.

We note that tattoo detection retrieval techniques can get highly affected by the change in sample quality, acquisition technique, etc. Motivated by the past competition on tattoo biometrics, namely Tatt-C and Tatt-E, and to further advance associated research, we host this competition focusing on the robustness of the evaluation pertained to tattoo detection and retrieval methodologies involving cross-dataset evaluation. The competition will focus on the following three tasks: (i) tattoo detection, (ii) tattoo retrieval, and (iii) tattoo sketch-based retrieval