DRNet: Decomposition and Reconstruction Network for Remote Physiological
Measurement
- URL: http://arxiv.org/abs/2206.05687v1
- Date: Sun, 12 Jun 2022 07:40:10 GMT
- Title: DRNet: Decomposition and Reconstruction Network for Remote Physiological
Measurement
- Authors: Yuhang Dong, Gongping Yang, Yilong Yin
- Abstract summary: Existing methods are generally divided into two groups.
The first focuses on mining the subtle volume pulse (BVP) signals from face videos, but seldom explicitly models the noises that dominate face video content.
The second focuses on modeling noisy data directly, resulting in suboptimal performance due to the lack of regularity of these severe random noises.
- Score: 39.73408626273354
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Remote photoplethysmography (rPPG) based physiological measurement has great
application values in affective computing, non-contact health monitoring,
telehealth monitoring, etc, which has become increasingly important especially
during the COVID-19 pandemic. Existing methods are generally divided into two
groups. The first focuses on mining the subtle blood volume pulse (BVP) signals
from face videos, but seldom explicitly models the noises that dominate face
video content. They are susceptible to the noises and may suffer from poor
generalization ability in unseen scenarios. The second focuses on modeling
noisy data directly, resulting in suboptimal performance due to the lack of
regularity of these severe random noises. In this paper, we propose a
Decomposition and Reconstruction Network (DRNet) focusing on the modeling of
physiological features rather than noisy data. A novel cycle loss is proposed
to constrain the periodicity of physiological information. Besides, a
plug-and-play Spatial Attention Block (SAB) is proposed to enhance features
along with the spatial location information. Furthermore, an efficient Patch
Cropping (PC) augmentation strategy is proposed to synthesize augmented samples
with different noise and features. Extensive experiments on different public
datasets as well as the cross-database testing demonstrate the effectiveness of
our approach.
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