On Fine-grained Temporal Emotion Recognition in Video: How to Trade off Recognition Accuracy with Annotation Complexity?

Fine-grained emotion recognition is the process of automatically identifying the emotions of users at a fine granularity level, typically in the time intervals of 0.5s to 4s according to the expected duration of emotions. Previous work mainly focused on developing algorithms to recognize only one emotion for a video based on the user feedback...

Heart Rate Estimation in Intense Exercise Videos using Temporal Superpixels

Estimation of physiological signals like heart rate using remote photoplethysmography (rPPG) provides non-contact health monitoring with important applications in remote healthcare, patient interaction, and sport related activities. Existing rPPG approaches can robustly measure heart rate from facial videos under some degree of motion through...