Suhail: A Deep Learning-Based System for Identifying Missing People

Abstract

Many people become missing in Saudi Arabia every day, including children, young people, and the mentally ill as well as the elderly with Alzheimer’s. There are many missing people cases that are still unsolved. In Saudi Arabia, people use social media platforms such as Twitter to report missing people cases. The application of deep learning has been successful in a wide range of fields including computer vision and machine vision. In particular, face recognition techniques are effective in saving time and effort, especially when searching for missing individuals. Hence, the goal of this research is to solve this issue by developing a deep learning-based system for identifying missing individuals. This paper introduces a new system called Suhail. The system has been implemented and developed using Android Studio and open-source libraries such as TensorFlow. First, users or governments can report missing persons by uploading photos. Updates and information will then be shared with the rest of the system’s users (volunteers). Once a volunteer discovers a suspect, they scan their face using camera. Then, our application uses face recognition techniques to compare the suspect's photo with photos from the repository. Finally, once a comparison is found, our application contacts the suspect’s family, informs them of his location and then notifies the police that a missing person has been located. By using our application and face recognition systems, we help families and police locate and reach a missing person which saves time and effort. In this study, 759 participants were enrolled to evaluate the performance of the Suhail system. Engagement, aesthetics, and functionality are used to evaluate the user experience. The results of the experiment show that users enjoy the new features of the application and that the system is simple to use. Moreover, the system would help governments and individuals identify missing people faster.