  Non-verbal communication plays a particularly important role in a wide range
of scenarios in Human-Robot Interaction (HRI). Accordingly, this work addresses
the problem of human gesture recognition. In particular, we focus on head and
eye gestures, and adopt an egocentric (first-person) perspective using eyewear
cameras. We argue that this egocentric view may offer a number of conceptual
and technical benefits over scene- or robot-centric perspectives. A
motion-based recognition approach is proposed, which operates at two temporal
granularities. Locally, frame-to-frame homographies are estimated with a
convolutional neural network (CNN). The output of this CNN is input to a long
short-term memory (LSTM) to capture longer-term temporal visual relationships,
which are relevant to characterize gestures. Regarding the configuration of the
network architecture, one particularly interesting finding is that using the
output of an internal layer of the homography CNN increases the recognition
rate with respect to using the homography matrix itself. While this work
focuses on action recognition, and no robot or user study has been conducted
yet, the system has been designed to meet real-time constraints. The
encouraging results suggest that the proposed egocentric perspective is viable,
and this proof-of-concept work provides novel and useful contributions to the
exciting area of HRI.
