Advancements in Face Alignment Evaluation for Contact-less Vital Sign Detection

The emergence of remote vital sign measurement techniques has provided an alternative approach for monitoring vital signs without direct physical contact. However, the performance of contactless methods such as remote photoplethysmography are dependent on the accuracy of face detection algorithms. The misalignment of the face pixels from frame to frame can introduce jitters in the generated rPPG signals, and in turn, interfere with vital sign estimations. Nonetheless, the investigation into the performance of face detectors mostly focused on quantifying the accuracy of face landmarks based on an individual image. How to assess facial alignments across video frames has largely remained unknown and understudied. To address this issue, this paper introduced three novel metrics for assessing face alignment in remote vital sign detection: (1) Circular Radius, (2) Mean Offset, and (3) Percentage of Impacted Pixels. We evaluated two face detectors using proposed metrics in static and motion scenarios, where static represents no facial movement and motion scenarios involve facial movements induced by breathing. Our experiments demonstrated that employing the superior face detector recommended by our metrics resulted in a noteworthy 12.5% reduction in second-level mean absolute error and a corresponding 3.0% improvement in 5%-accuracy for remote heart rate estimation.