University of Oulu

Z. Yu, X. Li, X. Niu, J. Shi and G. Zhao, "AutoHR: A Strong End-to-End Baseline for Remote Heart Rate Measurement With Neural Searching," in IEEE Signal Processing Letters, vol. 27, pp. 1245-1249, 2020, doi: 10.1109/LSP.2020.3007086

AutoHR : a strong end-to-end baseline for remote heart rate measurement with neural searching

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Author: Yu, Zitong1; Li, Xiaobai1; Niu, Xuesong2,3;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 90014, Finland
2Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
3University of Chinese Academy of Sciences, Beijing 100049, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 5.9 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-02-19


Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e.g., remote healthcare). Existing end-to-end rPPG and heart rate (HR) measurement methods from facial videos are vulnerable to the less-constrained scenarios (e.g., with head movement and bad illumination). In this letter, we explore the reason why existing end-to-end networks perform poorly in challenging conditions and establish a strong end-to-end baseline (AutoHR) for remote HR measurement with neural architecture search (NAS). The proposed method includes three parts: 1) a powerful searched backbone with novel Temporal Difference Convolution (TDC), intending to capture intrinsic rPPG-aware clues between frames; 2) a hybrid loss function considering constraints from both time and frequency domains; and 3) spatio-temporal data augmentation strategies for better representation learning. Comprehensive experiments are performed on three benchmark datasets, and we achieved superior performance on both intra- and cross-dataset testings.

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Series: IEEE signal processing letters
ISSN: 1070-9908
ISSN-E: 1558-2361
ISSN-L: 1070-9908
Volume: 27
Pages: 1245 - 1249
DOI: 10.1109/LSP.2020.3007086
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by the Academy of Finland for project MiGA (grant 316765), ICT 2023 project (grant 328115), and Infotech Oulu. As well, the authors wish to acknowledge CSC-IT Center for Science, Finland, for computational resources. (Corresponding author: Guoying Zhao.)
Academy of Finland Grant Number: 316765
Detailed Information: 316765 (Academy of Finland Funding decision)
328115 (Academy of Finland Funding decision)
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