Neuromorphic Event-based Facial Expression Recognition
This work addresses emotion recognition for applications requiring high temporal resolution, such as detecting subtle micro-expressions, but is incremental as it applies an existing neuromorphic method to a new dataset.
The authors tackled facial expression recognition by introducing NEFER, a dataset with paired RGB and event videos, and reported that the event-based approach doubled recognition accuracy compared to RGB data.
Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition. NEFER is composed of paired RGB and event videos representing human faces labeled with the respective emotions and also annotated with face bounding boxes and facial landmarks. We detail the data acquisition process as well as providing a baseline method for RGB and event data. The collected data captures subtle micro-expressions, which are hard to spot with RGB data, yet emerge in the event domain. We report a double recognition accuracy for the event-based approach, proving the effectiveness of a neuromorphic approach for analyzing fast and hardly detectable expressions and the emotions they conceal.