An efficient deep learning technique for facial emotion recognition

Emotion recognition from facial images is considered as a challenging task due to the varying nature of facial expressions. The prior studies on emotion classification from facial images using deep learning models have focused on emotion recognition from facial images but face the issue of performan...

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Bibliographic Details
Published in:Multimedia tools and applications 2022-01, Vol.81 (2), p.1649-1683
Main Authors: Khattak, Asad, Asghar, Muhammad Zubair, Ali, Mushtaq, Batool, Ulfat
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Deep learning
Emotion recognition
Emotions
Image classification
Machine learning
Multimedia Information Systems
Neural networks
Object recognition
Performance degradation
Special Purpose and Application-Based Systems
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Summary:Emotion recognition from facial images is considered as a challenging task due to the varying nature of facial expressions. The prior studies on emotion classification from facial images using deep learning models have focused on emotion recognition from facial images but face the issue of performance degradation due to poor selection of layers in the convolutional neural network model.To address this issue, we propose an efficient deep learning technique using a convolutional neural network model for classifying emotions from facial images and detecting age and gender from the facial expressions efficiently. Experimental results show that the proposed model outperformed baseline works by achieving an accuracy of 95.65% for emotion recognition, 98.5% for age recognition, and 99.14% for gender recognition.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-021-11298-w