Vol.13, No.2, May 2024.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

LunchByte: A Facial Recognition-Based Payment System for Health-Conscious Elementary Student Meals


Rsha Mirza, Hind Bitar, Ayman Alfahid, Yasmine Ajzaji, Renad Khayyat, Maryam Saadawi, Sadeem Alhujairi


© 2024 Rsha Mirza, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 13, Issue 2, Pages 1141-1156, ISSN 2217-8309, DOI: 10.18421/TEM132-29, May 2024.


Received: 27 November 2023.

Revised:   02 March 2024.
Accepted: 11 March 2024.
Published: 28 May 2024.




A significant number of elementary school students experience challenges related to lunch, including forgetting lunch money, lacking awareness of their individual dietary needs, and the importance of balanced nutritional choices for their overall health. Numerous parents and guardians struggle to monitor their children at school, control their purchasing behaviors, and maintain their health. Therefore, this research aims to enhance the efficiency and monitoring of elementary school students' lunch choices. LunchByte, a face recognition system developed in this study assists elementary in monitoring lunch expenses concerning their dietary restrictions. The main feature of LunchByte is using a student’s face print to generate a custom menu tailored to their allergies/diet restrictions and allowing them to pay with a prepaid balance added by their guardians. The evaluation of this system involves conducting model testing to assess the accuracy of facial recognition, integration testing to examine the interaction between different components of the system, and usability testing with end-users to evaluate the user experience. The results of these tests indicate that the system achieves high levels of accuracy and user satisfaction. The study is important because it has the potential to improve the overall health of young children.


Keywords –Artificial intelligence, face recognition, deep learning, school lunch, allergy.



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