摘要: 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 photoplethy... 展开 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 photoplethys-mography (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 bio-metric 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. 收起