UCLA

Real world scenes and objects have diverse visual appearance. Such diversity stems from the fundamental physics in how light interacts with matter, across different weather conditions, object types, and even people. These appearance variations mesmerize human beings, but puzzle artificial vision systems, which cannot generalize to such diversity. Through this thesis, we look at one such case of biased performance over diversity- camera based remote heart rate (HR) estimation. HR is an essential clinical measure for the assessment of cardiorespiratory instability. The growing telemedicine market opens up the urgent requirement for scalable yet affordable remote HR estimation. However, existing computer vision methods that estimate HR from facial videos exhibit biased performance against dark skin tones. This is a major concern, since communities of color are disproportionately affected by both COVID-19 and cardiovascular disease. We identify and model the origin of this bias and present a novel physics-driven algorithm that boosts performance on darker skin tones in our reported data. We assess the performance of our method through the creation of the first telemedicine-focused remote vital signs dataset, the VITAL dataset. 432 videos (~864 minutes) of 54 subjects with diverse skin tones are recorded under realistic scene conditions with corresponding vital sign data. Our method mitigates errors due environmental conditions and imparts unbiased performance gains across skin tones, setting the stage for making non-contact HR sensing technologies a viable reality for patients across skin tones.