An efficient deep learning technique for facial emotion recognition

Author First name, Last name, Institution

Asad Khattak, Zayed University
Muhammad Zubair Asghar, Gomal University
Mushtaq Ali, Hazara University
Ulfat Batool, Gomal University

ORCID Identifiers

0000-0003-3320-2074

Document Type

Article

Source of Publication

Multimedia Tools and Applications

Publication Date

10-9-2021

Abstract

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.

DOI Link

10.1007/s11042-021-11298-w

Publisher

Springer Nature

Disciplines

Computer Sciences

Keywords

Facial emotion recognition, Deep learning, CNN, Age recognition, Gender recognition

Scopus ID

85116732541

Recommended Citation

Khattak, Asad; Asghar, Muhammad Zubair; Ali, Mushtaq; and Batool, Ulfat, "An efficient deep learning technique for facial emotion recognition" (2021). All Works. 4608.
https://zuscholars.zu.ac.ae/works/4608

Indexed in Scopus

yes

Open Access

no