Head and eye egocentric gesture recognition for human-robot interaction using eyewear cameras

TL;DR

This paper presents a novel egocentric gesture recognition system using eyewear cameras, combining CNNs and LSTMs to improve human-robot interaction by recognizing head and eye gestures in real-time.

Contribution

It introduces a new egocentric approach for gesture recognition with a dual-temporal neural network architecture, demonstrating improved accuracy over traditional methods.

Findings

Using internal CNN layer outputs enhances recognition accuracy.

The system operates in real-time, suitable for HRI applications.

Egocentric perspective proves viable for gesture recognition.

Abstract

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.