Performance of an open source facial recognition system for unique patient matching in a resource-limited setting
Kitayimbwa, John M.
Were, Martin C.
International Journal of Medical Informatics
Background: The lack of unique patient identifiers is a challenge to patient care in developing countries. Probabilistic and deterministic matching approaches remain sub-optimal. However, affordable and scalable biometric solutions have not been rigorously evaluated in these settings. Methods: We implemented and evaluated performance of an open-source facial recognition system, OpenFace, integrated within a nationally-endorsed electronic health record system in Western Kenya. Patients were first enrolled via facial images, and later matched via the system. Accuracy of facial recognition was evaluated using Sensitivity; False Acceptance Rate (FAR); False Rejection Rate (FRR); Failure to Capture Rate (FTC) and Failure to Enroll Rate (FTE). 103 patients (mean age 37.8, 49.5% female) were enrolled. Results: The system had a sensitivity of 99.0%, FAR<1%, FRR 0.00, FTC 0.00 and FTE 0.00. Wearing spectacles did not affect performance. Conclusion: An open source facial recognition system correctly and accurately identified almost all patients during the first match.
This is a research article assesses the lack of unique patient identifiers is a challenge to patient care in developing countries.
Biometrics , Patient matching , Unique patient identifier , Facial identification
Kitayimbwa. John M. 2020 Performance of an open source facial recognition system for unique patient matching in a resource-limited setting. International Journal of Medical Informatics Vol. 141 Elsevier https://doi.org/10.1016/j.ijmedinf.2020.104180