This talk presents research on estimating health status from facial video analysis, focusing on non-invasive approaches to support well-being. The talk introduces robust estimation methods from facial videos for health- and well-being related indicators and real-time systems capable of simultaneously estimating vital signs-including heart rate, respiration, blood oxygen level, blood pressure, and pulse variability. It also explains the feasibility of estimating states such as drowsiness and stress from facial videos. These techniques utilize widely available devices such as smartphones and PCs, enabling continuous and low-burden monitoring in daily life. By demonstrating the feasibility of vision-based health estimation across various contexts, this work highlights its potential for workplace well-being, preventive healthcare, and telemedicine applications. Ultimately, this research aims to develop solutions that leverage cutting-edge technology to promote well-being, contributing to a future where people can lead healthier and more fulfilling lives-both physically and mentally.

Daily monitoring of intra-personal facial changes associated with health and emotional conditions has great potential to be useful for medical, healthcare, and emotion recognition fields. However, the approach for capturing intra-personal facial changes is relatively unexplored due to the difficulty of collecting temporally changing face images. In this paper, we propose a facial representation learning method using synthetic images for comparing faces, called ComFace, which is designed to capture intra-personal facial changes. For effective representation learning, ComFace aims to acquire two feature representations, i.e., inter-personal facial differences and intra-personal facial changes. The key point of our method is the use of synthetic face images to overcome the limitations of collecting real intra-personal face images. Facial representations learned by ComFace are transferred to three extensive downstream tasks for comparing faces: estimating facial expression changes, weight changes, and age changes from two face images of the same individual. Our Com-Face, trained using only synthetic data, achieves comparable to or better transfer performance than general pretraining and state-of-the-art representation learning methods trained using real images.

Video-based heart and respiratory rate measurements using facial videos are more useful and user-friendly than traditional contact-based sensors. However, most of the current deep learning approaches require ground-truth pulse and respiratory waves for model training, which are expensive to collect. In this paper, we propose CalibrationPhys, a self-supervised video-based heart and respiratory rate measurement method that calibrates between multiple cameras. CalibrationPhys trains deep learning models without supervised labels by using facial videos captured simultaneously by multiple cameras. Contrastive learning is performed so that the pulse and respiratory waves predicted from the synchronized videos using multiple cameras are positive and those from different videos are negative. CalibrationPhys also improves the robustness of the models by means of a data augmentation technique and successfully leverages a pre-trained model for a particular camera. Experimental results utilizing two datasets demonstrate that CalibrationPhys outperforms state-of-the-art heart and respiratory rate measurement methods. Since we optimize camera-specific models using only videos from multiple cameras, our approach makes it easy to use arbitrary cameras for heart and respiratory rate measurements.