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Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study.
Dhwani Mehta1, Mohammad Faridul Haque Siddiqui2, Ahmad Y Javaid3
1Electrical Engineering and Computer Science Department, The University of Toledo, 2801 W Bancroft St, MS 308, Toledo, OH 43606, USA. dhwani.mehta@utoledo.edu.
Sensors (Basel, Switzerland)
|April 24, 2019
Summary
This study introduces a comparative analysis of facial emotion recognition techniques, incorporating emotion intensity estimation. It evaluates Gabor filters, Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) for feature extraction, alongside Support Vector Machine (SVM), Random Forest (RF), and Nearest Neighbor Algorithm (kNN) classifiers.
Area of Science:
- Computer Science
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Automatic facial emotion recognition is crucial for behavioral biometrics and human-machine interaction.
- Existing methods often neglect emotion intensity and joint multi-class facial behavior modeling.
Purpose of the Study:
- To recognize emotions and estimate their intensities simultaneously.
- To conduct a comparative study of feature extraction and classification algorithms for facial emotion and intensity recognition.
Main Methods:
- Feature extraction using Gabor filters, Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP).
- Classification using Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbor (kNN) algorithms.
- Comparative analysis of these methods on facial emotion databases.
Main Results:
- The study successfully achieved emotion recognition and intensity estimation for each recognized emotion.
- Demonstrated the effectiveness of the comparative approach in evaluating different algorithms.
Conclusions:
- The comparative study provides valuable insights into algorithms for facial emotion and intensity recognition.
- Findings support the potential application of these methods in real-time behavioral analysis.
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