Abstract
The loss of a child is a profound event that poses substantial emotional, psychological, and logistical challenges for families. Conventional child recovery methods, like search parties and media campaigns, often falter due to delayed responses, limited outreach, and ineffective data management. These methods struggle to handle vast information or employ modern tools efficiently. In response, this study introduces Baseer, a system merging drone technology with AI to enhance the efficacy of locating missing children. Baseer utilizes drones with high-resolution imaging systems for rapid aerial surveillance, expanding search capabilities beyond standard ground-based approaches. AI algorithms analyze real-time images for automated identification of potential matches based on the child's facial features or recognizable patterns. The system includes a user-friendly mobile app for prompt reporting of missing children by guardians, initiating immediate search operations. By combining automated data collection, advanced imaging, and real-time analysis, Baseer overcomes traditional method limitations, offering a scientifically sound and efficient child recovery framework. The technical evaluation of Baseer involved unit testing and usability testing. Qualitative assessments of usability and ease of use were conducted with a group of six participants, indicating that Baseer is affective, fast, and user-friendly.
Keywords
Drones, lost-child, AI, Image recognition
Article Type
Article
First Page
41
Last Page
51
Publication Date
4-30-2025
Recommended Citation
Attiah, Afraa and Hakeem, Abeer
(2025)
"Baseer: A Drone-based System Leveraging Facial and Pattern Recognition for Efficient Real-Time Child Recovery,"
Journal of King Abdulaziz University: Computing and Information Technology Sciences: Vol. 14:
Iss.
1, Article 4.
DOI: https://doi.org/10.64064/1658-6336.1004