Multi-face emotion detection for effective Human-Robot Interaction
This work addresses the problem of enhancing human-robot interaction through emotion recognition, but it appears incremental as it focuses on optimizing existing methods for a specific application.
The research tackled real-time multi-face emotion detection for humanoid robots by developing and evaluating deep neural network models, achieving promising results with a trade-off between accuracy and memory footprint for mobile implementation.
The integration of dialogue interfaces in mobile devices has become ubiquitous, providing a wide array of services. As technology progresses, humanoid robots designed with human-like features to interact effectively with people are gaining prominence, and the use of advanced human-robot dialogue interfaces is continually expanding. In this context, emotion recognition plays a crucial role in enhancing human-robot interaction by enabling robots to understand human intentions. This research proposes a facial emotion detection interface integrated into a mobile humanoid robot, capable of displaying real-time emotions from multiple individuals on a user interface. To this end, various deep neural network models for facial expression recognition were developed and evaluated under consistent computer-based conditions, yielding promising results. Afterwards, a trade-off between accuracy and memory footprint was carefully considered to effectively implement this application on a mobile humanoid robot.