Work (Computer Vision + Computational Imaging)

Adaptive RGB/NIR Signal Fusion for Heart Rate Estimation

K. Kurihara, D. Sugimura and T. Hamamoto, IEEE TIP, IEEE, 2021. [paper]

K. Kurihara, D. Sugimura and T. Hamamoto, IEEE ICIP, IEEE, 2019. [paper]

K. Kurihara, Y. Maeda, D. Sugimura and T. Hamamoto, IEEE ICIP, IEEE, 2024. [paper]

Abstract

We propose a non-contact heart rate (HR) estimation method that is robust to various situations, such as bright, low-light, and varying illumination scenes. We utilize a camera that records red, green, and blue (RGB) and near-infrared (NIR) information to capture the subtle skin color changes induced by the cardiac pulse of a person. The key novelty of our method is the adaptive fusion of RGB and NIR signals for HR estimation based on the analysis of background illumination variations. RGB signals are suitable indicators for HR estimation in bright scenes. Conversely, NIR signals are more reliable than RGB signals in scenes with more complex illumination, as they can be captured independently of the changes in background illumination. By measuring the correlations between the lights reflected from the background and facial regions, we adaptively utilize RGB and NIR observations for HR estimation. The experiments demonstrate the effectiveness of the proposed method.