Automatic Face Recognition for Forensic Identification of Persons Deceased in Humanitarian Emergencies

Abstract

Forensic scientists often need to identify deceased people. The identification process mainly consists of the analysis of the DNA, dental records, and physical appearance. In humanitarian emergencies, the antemortem documentation needed for forensic analyses may be limited. In this context, face recognition plays a relevant role since antemortem pictures of missing persons are commonly made available by their families. Therefore, automatic recognition systems could be of paramount importance for reducing the search time in databases of face images and for providing a second opinion to the scientists. However, there are only few preliminary studies on automatic face recognition methods for forensic applications, and none of the works in the literature consider problems related to humanitarian emergencies. In this paper, we propose the first study on automatic face recognition for humanitarian emergencies. Specifically, we propose a recognition methodology and we analyze the accuracy of different biometric methods based on deep learning strategies for a real study case. In particular, the considered data regard recent deaths of migrants in Mediterranean Sea. The obtained results are satisfactory, and suggest that automatic recognition methods based on deep learning strategies could be effectively adopted as support tools for forensic identification.