Mr. Arya Kumar Profile

Arya Kumar

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Proceedings Article | 29 April 2020 Presentation + Paper

Standoff heart rate estimation from video: a review

Yunbin Deng, Arya Kumar

Proceedings Volume 11399, 1139906 (2020) https://doi.org/10.1117/12.2560683

KEYWORDS: Heart, Video, Skin, Sensors, Signal to noise ratio, Light sources and illumination, RGB color model, Cameras

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Recent breakthroughs in EO/IR sensing, real-time signal processing, and deep machine learning technologies have enabled standoff heart rate estimation from facial and body video. This technology is also known as remote photoplethysmography (rPPG). Research and development of rPPG has attracted much attention recently. This paper gives a timely review of this fast-paced field to give the researcher, engineer, and graduate student a quick grasp of the recent advancement of rPPG. We first review two rPPG design approaches: color variation based and motion-based detections. To enable rPPG for less constrained use cases, various signal processing and machine learning algorithms are developed to handle signal variabilities introduced by lighting source, view angle, and subject motion. To help newcomers quickly start work in this field, we then describe some existing rPPG research datasets, open-source rPPG research tools, and some demonstration systems. Six commonly used rPPG algorithm evaluation metrics are described to evaluate and visualize the research advance in this field. As the rPPG technology matures, more application domains become possible. We cover six applications of rPPG in commercial, security, and defense domains, including emerging applications in biometric liveness and video media authenticity. Finally, we outline some challenges yet to overcome, especially in the domain of security and defense. These challenges include unconstrained outdoor environment, rPPG form air-platform, night time operation, moving and non-cooperative subjects. These challenges require special algorithmic considerations.

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