Document Type
Article
Source of Publication
Mathematical Biosciences and Engineering
Publication Date
1-1-2023
Abstract
Facial expression is a type of communication and is useful in many areas of computer vision, including intelligent visual surveillance, human-robot interaction and human behavior analysis. A deep learning approach is presented to classify happy, sad, angry, fearful, contemptuous, surprised and disgusted expressions. Accurate detection and classification of human facial expression is a critical task in image processing due to the inconsistencies amid the complexity, including change in illumination, occlusion, noise and the over-fitting problem. A stacked sparse auto-encoder for facial expression recognition (SSAE-FER) is used for unsupervised pre-training and supervised fine-tuning. SSAE-FER automatically extracts features from input images, and the softmax classifier is used to classify the expressions. Our method achieved an accuracy of 92.50% on the JAFFE dataset and 99.30% on the CK+ dataset. SSAE-FER performs well compared to the other comparative methods in the same domain.
DOI Link
ISSN
Publisher
American Institute of Mathematical Sciences (AIMS)
Volume
20
Issue
5
First Page
8208
Last Page
8225
Disciplines
Computer Sciences
Keywords
classification, deep learning, facial expression recognition, machine learning, stacked sparse auto-encoder
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Ahmad, Mubashir; Saira; Alfandi, Omar; Khattak, Asad Masood; Qadri, Syed Furqan; Saeed, Iftikhar Ahmed; Khan, Salabat; Hayat, Bashir; and Ahmad, Arshad, "Facial expression recognition using lightweight deep learning modeling" (2023). All Works. 5741.
https://zuscholars.zu.ac.ae/works/5741
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series