## 1 Introduction
Odontological data, fingerprints, DNA samples and serial numbers of medical implants, e.g., pacemakers, are considered basic and unique identifiers according to the DVI procedure. In practice, identification is very often circumstantial and the result of a combination of ambiguous identifiers such as medical records, clothing, jewelry, tattoos, and visual verification by loved ones. Each case of ambiguous identification is resolved on a case-by-case basis. Once the identity is agreed upon and deemed reliable, an Identification Board (IB) is convened, with the chairman having primary responsibility for the identification conducted. They receive a comparison report and a certificate of identification, based on which they accept or reject the identification request for a particular case. Acceptance means that the identity of the deceased is clearly established and results in the issuance of a death certificate and the start of preparations for the transfer of the body to the family. Detailed information on each step of the victim identification procedure is described in the current version of the DVI Guide [15]. Depending on the extent of the disaster, verifying the identity of victims according to the DVI procedure can be very time- and labor-intensive, as exemplified by the identification of victims of the 2004 Indian Ocean coastal tsunami, which took an estimated 217,000 lives. More than 2000 specialists from 31 countries were involved in the process of identifying the remains [11]. Disaster victim identification poses a significant challenge to forensic teams due to the complexity and scale of many disaster scenarios. Traditional methods often rely heavily on manual comparisons of PostMortem (PM) and AnteMortem (AM) data, which is time-consuming, error-prone, and emotionally taxing for operators. The solutions described in the next chapter provide partial support for DVI tasks, but often lack the integration of modern technologies such as neural network-based identification and mobile applications to streamline and automate the processes. Furthermore, these systems often lack the ability to enter data on-site, which leads to delays in the overall identification workflow. The presented system addresses many of these limitations and offers a unique, comprehensive solution that improves the efficiency, accuracy, and availability of disaster victim identification processes. The main contributions of this work are as follows:  
* Design and implementation of a Disaster Victim Identification (DVI) module based on deep convolutional neural networks, enabling automatic comparison of post-mortem (PM) and ante-mortem (AM) photographic data. * Adapting and implementing a deep learning metrics technique to improve the efficiency and accuracy of matching AM and PM datasets. * Development of a mobile application that facilitates data collection directly at the disaster site, even in the absence of an internet connection, enabling real-time processing and synchronization of PM data. * Partial automation of the DVI process, which reduces the need for manual comparison of photographic materials and speeds up victim identification. The remainder of the paper is organized as follows: Section 2 describes the approaches and systems currently in use to support the DVI procedure and the motivation behind the development of the proposed ID Victim system. Section 3 presents the methodology used regarding both the neural network architecture developed and its compilation, as well as the implementation details for the mobile application. Section 4 presents and describes the results of the testing of the system and the neural network metrics. Section 5 presents the conclusion and summary regarding the research conducted.