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rPPG-Toolbox: Deep Remote PPG Toolbox

Xin Liu · Girish Narayanswamy · Akshay Paruchuri · Xiaoyu Zhang · Jiankai Tang · Yuzhe Zhang · Roni Sengupta · Shwetak Patel · Yuntao Wang · Daniel McDuff

Great Hall & Hall B1+B2 (level 1) #332
[ ] [ Project Page ]
[ Paper [ Poster [ OpenReview
Tue 12 Dec 8:45 a.m. PST — 10:45 a.m. PST


Camera-based physiological measurement is a fast growing field of computer vision. Remote photoplethysmography (rPPG) utilizes imaging devices (e.g., cameras) to measure the peripheral blood volume pulse (BVP) via photoplethysmography, and enables cardiac measurement via webcams and smartphones. However, the task is non-trivial with important pre-processing, modeling and post-processing steps required to obtain state-of-the-art results. Replication of results and benchmarking of new models is critical for scientific progress; however, as with many other applications of deep learning, reliable codebases are not easy to find or use. We present a comprehensive toolbox, rPPG-Toolbox, unsupervised and supervised rPPG models with support for public benchmark datasets, data augmentation and systematic evaluation:

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