Detecting Pulse Wave From Unstable Facial Videos Recorded From Consumer-Level Cameras: A Disturbance-Adaptive Orthogonal Matching Pursuit

Objective: Modern consumer-level cameras can detect subtle changes in human facial skin color due to varying blood flow; they are beginning to be used as noncontact devices to detect pulse waves. Little, however, do we know about their capacity to perform pulse wave detection when the recorded faces are unstable. Methods: Here, we propose a novel method that can extract pulse waves from videos with drastic facial unsteadiness such as head twists and alternating expressions. The method first uses chrominance characteristics in multiple facial sub-regions to construct a raw pulse matrix. Subsequently, it employs a disturbance-adaptive orthogonal matching pursuit (DAOMP) algorithm to recover the underlying pulse matrix corrupted by facial unsteadiness. Results: To evaluate the efficacy of the method, we perform analyses on two datasets including 268 samples from 67 testing subjects. The results demonstrate that the proposed method outperforms state-of-the-art algorithms, especially in the terrain where drastic facial unsteadiness is present. Conclusion: The proposed framework shows promise to achieve videos-based noncontact pulse wave detection from both steady and unsteady faces recorded by consumer-level cameras. Significance: By employing the proposed method, disturbance robustness in noncontact pulse wave detection can be significantly improved.