Rusoke, Blaise MarvinMusinguzi, DenisMiyingo, Simon PeterKatumba, Andrew2022-11-292022-11-292022Rusoke, Blaise Marvin; Musinguzi, Denis; Miyingo, Simon Peter; Katumba, Andrew (2022): Edge AI Face Recognition for Public Transport Fare Payment. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.21432000.v1https://doi.org/10.36227/techrxiv.21432000.v1https://nru.uncst.go.ug/handle/123456789/5535Face Recognition technology is chiefly concerned with accurately re-identifying individuals through the use of mathematical face representations. It presents a window of opportunity for the introduction of a fast, automated, seamless and easy to deploy form of biometric technology. In this research we design a fast, easy to use, and privacy oriented contactless payment system for public transportation that chiefly makes use of face recognition and internet of things technologies. We demonstrate a one-shot face recognition model and also prepare and test it for real-time inference on the edge. Our system makes use of a Siamese Model built on top of the Inception-Resnet V1 architecture with accuracy, precision and recall values of 93.81%, 90.91% and 97.35% on our validation set. The model was deployed on a Raspberry Pi 4 Model B with an Intel Neural Compute Stick 2. Inference was performed through the inference engine API of the OpenVINO toolkit on the Neural Compute Stick plugged into the Raspberry Pi. The system is composed of three other subsystems, i.e. the edge device, cloud database and user interface subsystems which work together to ensure that payment is complete in under 2 seconds.enSiamese NetworkEdge deviceFace RecognitionPayment systemArticle