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.

Yu Zitong, Li Xiaobai, Niu Xuesong, Shi Jingang, Zhao Guoying

Publication type:
A1 Journal article – refereed

Place of publication:

heart rate, neural architecture search, RPPG


Full citation:
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


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