An improved facial emotion recognition system using convolutional neural network for the optimization of human robot interaction
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View abstract on PubMed
Summary
This summary is machine-generated.This study demonstrates the feasibility of using machine learning and computer vision, specifically convolutional neural networks (CNNs), for accurate facial emotion recognition (FER). The developed software effectively identifies human emotions in images and videos, enhancing human-robot interactions (HRI).
Area Of Science
- Robotics and Artificial Intelligence
- Computer Vision
- Machine Learning
Background
- Robotics applications increasingly require sophisticated human-robot interactions (HRI) for effective functionality.
- Computer vision, particularly facial emotion recognition (FER), is crucial for enhancing HRI precision.
- Identifying human emotional states is key to developing more intuitive and responsive robotic systems.
Purpose Of The Study
- To develop and evaluate software capable of recognizing human emotions from images and videos using computer vision and machine learning (ML).
- To investigate the feasibility of training software based on emotional expressions for FER.
- To identify essential facial gestures for CNN-based FER systems.
Main Methods
- Utilized ML techniques and a digital image processing pipeline for emotion recognition.
- Developed and trained software incorporating convolutional neural networks (CNNs) for FER.
- Compared the proposed model's performance against established datasets: FER2013, RAF-DB, and CK+.
Main Results
- The developed software demonstrated the feasibility of recognizing emotions in human gestures.
- Achieved a high accuracy rate of approximately 95% on the CK+ dataset.
- Performance varied across datasets, with the FER2013 dataset yielding the lowest accuracy at around 64%.
Conclusions
- The study confirms the viability of CNNs for accurate facial emotion recognition.
- The findings contribute to advancing knowledge in neural networks and improving computer vision efficiency.
- Enhanced FER capabilities can significantly improve the quality of human-robot interactions in various applications.
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