|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM30252357X |
| 003 |
DE-627 |
| 005 |
20240229162356.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231225s2019 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2019.2947204
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1308.xml
|
| 035 |
|
|
|a (DE-627)NLM30252357X
|
| 035 |
|
|
|a (NLM)31647433
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Niu, Xuesong
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a RhythmNet
|b End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation
|
| 264 |
|
1 |
|c 2019
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 27.02.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status Publisher
|
| 520 |
|
|
|a Heart rate (HR) is an important physiological signal that reflects the physical and emotional status of a person. Traditional HR measurements usually rely on contact monitors, which may cause inconvenience and discomfort. Recently, some methods have been proposed for remote HR estimation from face videos; however, most of them focus on well-controlled scenarios, their generalization ability into less-constrained scenarios (e.g., with head movement, and bad illumination) are not known. At the same time, lacking large-scale HR databases has limited the use of deep models for remote HR estimation. In this paper, we propose an end-to-end RhythmNet for remote HR estimation from the face. In RyhthmNet, we use a spatial-temporal representation encoding the HR signals from multiple ROI volumes as its input. Then the spatial-temporal representations are fed into a convolutional network for HR estimation. We also take into account the relationship of adjacent HR measurements from a video sequence via Gated Recurrent Unit (GRU) and achieves efficient HR measurement. In addition, we build a large-scale multi-modal HR database (named as VIPL-HRVIPL-HR is available at: ), which contains 2,378 visible light videos (VIS) and 752 near-infrared (NIR) videos of 107 subjects. Our VIPL-HR database contains various variations such as head movements, illumination variations, and acquisition device changes, replicating a less-constrained scenario for HR estimation. The proposed approach outperforms the state-of-the-art methods on both the public-domain and our VIPL-HR databases
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Shan, Shiguang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Han, Hu
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Chen, Xilin
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2019) vom: 22. Okt.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g year:2019
|g day:22
|g month:10
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2019.2947204
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|j 2019
|b 22
|c 10
|