The aim of this study is to develop and evaluate software for detecting eye features based on standardized video recordings, with the ultimate goal of integrating it into the existing Philips Vital Signs Camera. The software consists of three modules: eye and iris detection, blink detection and pupil detection. It was developed using open-source libraries, with MediaPipe FaceMesh as the primary component. To validate the eye/iris and blink detection modules, the VSC-MEDlib dataset was used, which had previously been employed to evaluate the Philips Vital Signs Camera. For pupil detection, video recordings of a subject under various lighting conditions were analyzed.
For eye detection, 6 out of 7 test IDs achieved a 100% recognition rate. For iris detection, 5 out of 7
test IDs achieved a 100% recognition rate. Test ID 7 reached a 99% recognition rate. For both eye
detection and iris detection, the face mask condition showed a significant deviation (p < 0.01), partic-
ularly for individuals with darker skin colors (p < 0.1). After parameter optimization, the Bland–ltman
analysis showed that 84 out of 90 data points (93.3%) fell within the 95% limits of agreement, indicating good overall agreement between the software and the true blink counts, with a small number of outliers beyond the expected limits. Pupil detection accurately identified pupil position but consistently underestimated pupil size, especially when the pupil was small. Nevertheless, the software was able to detect appropriate pupil size changes in response to lighting conditions and demonstrated sensitivity to the pupillary light reflex. These findings indicate that reliable measurements of eye features can be obtained from standard video recordings. This opens up possibilities for non-invasive and remote patient monitoring, with potential integration into existing systems such as the Philips Vital Signs Camera. Future research could explore the combination of eye-tracking and neuromonitoring to expand the range of accessible physiological metrics.