Exploring Remote Physiological Signal Measurement under Dynamic Lighting Conditions at Night: Dataset, Experiment, and Analysis

TL;DR

This paper introduces DLCN, a large-scale dataset captured under dynamic nighttime lighting, to evaluate and improve remote physiological signal measurement methods in realistic, challenging conditions.

Contribution

It provides a new, diverse dataset for nighttime dynamic lighting scenarios and analyzes the robustness of existing rPPG algorithms in such environments.

Findings

Current rPPG methods face significant challenges under dynamic nighttime lighting.

The DLCN dataset reveals the limitations of existing algorithms in realistic conditions.

The study offers insights for developing more robust remote physiological measurement techniques.

Abstract

Remote photoplethysmography (rPPG) is a non-contact technique for measuring human physiological signals. Due to its convenience and non-invasiveness, it has demonstrated broad application potential in areas such as health monitoring and emotion recognition. In recent years, the release of numerous public datasets has significantly advanced the performance of rPPG algorithms under ideal lighting conditions. However, the effectiveness of current rPPG methods in realistic nighttime scenarios with dynamic lighting variations remains largely unknown. Moreover, there is a severe lack of datasets specifically designed for such challenging environments, which has substantially hindered progress in this area of research. To address this gap, we present and release a large-scale rPPG dataset collected under dynamic lighting conditions at night, named DLCN. The dataset comprises approximately 13 hours of video data and corresponding synchronized physiological signals from 98 participants, covering four representative nighttime lighting scenarios. DLCN offers high diversity and realism, making it a valuable resource for evaluating algorithm robustness in complex conditions. Built upon the proposed Happy-rPPG Toolkit, we conduct extensive experiments and provide a comprehensive analysis of the challenges faced by state-of-the-art rPPG methods when applied to DLCN. The dataset and code are publicly available at https://github.com/dalaoplan/Happp-rPPG-Toolkit.