An efficient deep learning technique for facial emotion recognition |
| |
Authors: | Khattak Asad Asghar Muhammad Zubair Ali Mushtaq Batool Ulfat |
| |
Affiliation: | 1.College of Technological Innovation, Zayed University, Abu Dhabi Campus, 144534, Abu Dhabi, UAE ;2.Institute of Computing and Information Technology, Gomal University, DIKhan (KP), Pakistan ;3.Department of Information Technology, Hazara University Mansehra, Dhodial, Pakistan ; |
| |
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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|