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Performance of an open source facial recognition system for unique patient matching in a resource-limited setting
Sight Ampamya1, John M Kitayimbwa2, Martin C Were3
1Institute of Biomedical Informatics, Moi University, Eldoret, Kenya.
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.
