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Non-Contact sensing is an emerging technology with applications across manyindustries from driver monitoring in vehicles to patient monitoring inhealthcare. Current state-of-the-art implementations focus on RGB video, butthis struggles in varying/noisy light conditions and is almost completelyunfeasible in the dark. Near Infra-Red (NIR) video, however, does not sufferfrom these constraints. This paper aims to demonstrate the effectiveness of analternative Convolution Attention Network (CAN) architecture, to regressphotoplethysmography (PPG) signal from a sequence of NIR frames. A combinationof two publicly available datasets, which is split into train and test sets, isused for training the CAN. This combined dataset is augmented to reduceoverfitting to the 'normal' 60 - 80 bpm heart rate range by providing the fullrange of heart rates along with corresponding videos for each subject. ThisCAN, when implemented over video cropped to the subject's head, achieved a MeanAverage Error (MAE) of just 0.99 bpm, proving its effectiveness on NIR videoand the architecture's feasibility to regress an accurate signal output.

Non-Contact NIR PPG Sensing through Large Sequence Signal Regression