Paper Info
Title
MANet: a Motion-Driven Attention Network for Detecting the Pulse from a Facial Video with Drastic Motions
Abstract
Video Photoplethysmography (VPPG) technique can detect pulse signals from facial videos, becoming increasingly popular due to its convenience and low cost. However, it fails to be sufficiently robust to drastic motion disturbances such as continuous head movements in our real life. A motion-driven attention network (MANet) is proposed in this paper to improve its motion robustness. MANet takes the frequency spectrum of a skin color signal and of a synchronous nose motion signal as the inputs, following by removing the motion features out of the skin color signal using an attention mechanism driven by the nose motion signal. Thus, it predicts frequency spectrum without components resulting from motion disturbances, which is finally transformed back to a pulse signal. MANet is tested on 1000 samples of 200 subjects provided by the 2nd Remote Physiological Signal Sensing (RePSS) Challenge. It achieves a mean inter-beat-interval (IBI) error of 122.80 milliseconds and a mean heart rate error of 7.29 beats per minute.
Year
DOI
Venue
2021
10.1109/ICCVW54120.2021.00270
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021)
Keywords
DocType
Volume
n/a
Conference
2021
Issue
ISSN
Citations 
1
2473-9936
0
PageRank 
References 
Authors
0.34
2
6
Name
Order
Citations
PageRank
Xuenan Liu122.06
Xuezhi Yang26110.46
Ziyan Meng300.34
Ye Wang400.34
Jie Zhang500.34
Alexander Wong635169.61