The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
RhythmNet: End-to-End Heart Rate Estimation From Face via Spatial-Temporal Representation
Open accessAbstract
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.
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Cited by
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
Qi, H., Guo, Q., Juefei-Xu, F., Xie, X., Ma, L., Feng, W., Liu, Y., & Zhao, J. (2020) · arXiv · doi:10.48550/arxiv.2006.07634 · Cited by 280
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
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Countering Spoof: Towards Detecting Deepfake with Multidimensional Biological Signals
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PhysFlow: Skin tone transfer for remote heart rate estimation through conditional normalizing flows
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“Some combine traditional methods with Convolutional Neural Networks (CNNs) to leverage advanced features [34, 36, 47].”
Method
Metadata
- Authors
- Niu, X., Shan, S., Han, H., & Chen, X.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2020
- Volume
- 29
- Pages
- 2409-2423
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2982196965
- Cited by
- 420
- References
- 41
- FWCI
- 11.28
- Type
- journal article
- Language
- en
- Open access
- green
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