AutoFER: PCA and PSO based automatic facial emotion recognition

Automatic emotion recognition is a critical part of human-machine interactions. Reflection of emotions and to develop its understanding is crucial to provide dealings across human beings and machine frameworks. This work determines an automatic system that distinguishes different emotions connoted o...

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Bibliographic Details
Published in:Multimedia tools and applications 2021, Vol.80 (2), p.3039-3049
Main Authors: Arora, Malika, Kumar, Munish
Format: Article
Language:English
Subjects:
Acceptance
Accuracy
Anger
Classification
Computer Communication Networks
Computer Science
Computers
Data Structures and Information Theory
Emotion recognition
Emotions
Feature extraction
Happiness
Multimedia
Multimedia Information Systems
Neural networks
Optimization
Principal components analysis
Rejection rate
Special Purpose and Application-Based Systems
Swarm intelligence
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Summary:Automatic emotion recognition is a critical part of human-machine interactions. Reflection of emotions and to develop its understanding is crucial to provide dealings across human beings and machine frameworks. This work determines an automatic system that distinguishes different emotions connoted on the face. The framework is deliberated to apply the hybridization of feature extraction and optimization using PCA and PSO, respectively, to accomplish a high precision rate. PCA is used to get high-quality feature vectors for each category of emotion. Swarm intelligence, optimization is applied to get an optimized feature vector which is essential for classifying the features in the testing phase. For exploratory work, the authors have considered the Japanese Female Facial Expression (JAFFE) dataset. A maximum classification rate of 94.97% is achieved with the proposed technique. The proposed framework execution is assessed in terms of the false rejection rate, false acceptance rate, and accuracy.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-09726-4