Multi-face emotion detection for effective Human-Robot Interaction

TL;DR

This paper presents a multi-face emotion detection system for mobile humanoid robots, utilizing deep neural networks to recognize and display real-time emotions, thereby enhancing human-robot interaction.

Contribution

It introduces a novel facial emotion detection interface integrated into a mobile humanoid robot, with optimized neural network models balancing accuracy and resource constraints.

Findings

Deep neural networks effectively recognize multiple facial emotions.

Trade-offs between accuracy and memory footprint are crucial for mobile deployment.

The system enhances real-time emotion understanding in human-robot interaction.

Abstract

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.