Performance of an Open Source Facial Recognition System for Unique Patient Matching in a Resource-Limited Setting
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Medical Informatics
Abstract
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.
Description
This is a research article assesses the lack of unique patient identifiers is a challenge to patient care in developing countries.
Keywords
Biometrics, Patient matching, Unique patient identifier, Facial identification
Citation
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