CoSTHR: A Heart Rate Estimating Network With Adaptive Color Space Transformation

Abstract

Heart rate (HR) is one of the most important physiological signals of a person. Remote HR estimation derives subtle color variations from one’s facial video captured by a camera. The periodic signal caused by heart activity is then extracted from the color variations, resulting in heart rate. The advantages of remote methods show in their contactlessness, comfortableness, and impalpability. Due to the very subtle color changes of the face, remote methods are quite sensitive to illumination variations and motion artifacts. In current studies, researchers have found that the results achieved from experiments conducted on different color spaces vary significantly, suggesting that the choice of color space has an important impact on HR estimation. However, it is difficult to propose an optimum color space that is appropriate for the HR estimation task. Additionally, there is a lack of specialized training strategies for color space projection tasks as well as combinations with the state-of-the-art mechanisms. In this article, we propose a HR estimating network with adaptive color space transformation, abbreviated as CoSTHR, to address these challenges. The network uses color space transformation (CoST) layers to learn the optimum color space for HR estimation task, based on which the attention convolutional neural network can focus on the informative channels and extract the HR. Specialized training strategies are further introduced to enhance the model performance, yielding more accurate results. Experiments on dataset VIPL-HR show that the proposed CoSTHR achieves better results than the state-of-the-art algorithms.