Supervised Image Decomposition for Human Heart Rate Measurement Under Low-Light Environments

A supervised image decomposition algorithm is proposed in this study for human heart rate measurement under low-light environments. First, the comparative human face image pairs under different light intensities are captured, and special image capturing and matching algorithms are designed in this procedure. Then, a multilayer image decomposition network is constructed and trained to obtain the correlation of the images under different lighting intensities. Specifically, a loss function, including illumination loss, illumination smoothness loss, and interframe loss, is designed for network optimization. A heart rate measurement algorithm runs at last for the calculation of heart rate. Verification experiments under different human-camera distances and light intensities confirm the effectiveness of the proposed method. Specifically, the proposed method can be used for stale and effective human heart rate measurement when the human–camera distance is within 0.8 m and the light intensity is higher than 400 lux.