MAHYA: Facial Recognition-Based Pilgrim Identification System for Enhanced Health Monitoring and Assistance

During the Hajj season, Saudi Arabia experiences the arrival of millions of pilgrims from diverse linguistic and geographical backgrounds. This influx poses significant challenges for emergency medical care services. The primary objective of this study is to explore the technological shortcomings and difficulties encountered by healthcare teams during such large-scale gatherings and to propose improvements for more effective emergency medical response systems. This study introduces MAHYA, a mobile health technology application designed to enhance emergency medical responses. MAHYA integrates advanced facial recognition technology, utilizing Inception ResNet V1 and Siamese network algorithms, to quickly and accurately identify individuals and retrieve their medical histories. This quick access to vital medical information is crucial for timely and efficient emergency medical care. The app incorporates a few-shot learning approach to bolster its facial recognition capabilities, which is vital to manage the large number of pilgrims. Further technical aspects of MAHYA include its use of Flask for back-end operations, Python for data processing, and NGROK to ensure secure external connectivity. These features collectively empower the application to offer a highly effective, secure, and adaptive facial recognition service, tailored for the dynamic and densely populated environment of the Hajj. The findings of the deployment of this application indicate a substantial improvement in the operational efficiency of healthcare professionals on the ground, leading to faster response times and improved overall quality of emergency medical services.

@article{Albalawi2025,
  title     = {MAHYA: Facial Recognition-Based Pilgrim Identification System for Enhanced Health Monitoring and Assistance},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {3},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Shahad Albalawi and Lujin Alamri and Jumanah Atut and Shatha Albalawi and Reem Haddaddi and A’aeshah Alhakamy},
  doi       = {10.14569/IJACSA.2025.0160391},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160391}
}