  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.
