Manta Ray Foraging Optimization with Transfer Learning Driven Facial Emotion Recognition

Anwer Mustafa Hilal (), Dalia H. Elkamchouchi, Saud S. Alotaibi, Mohammed Maray, Mahmoud Othman, Amgad Atta Abdelmageed, Abu Sarwar Zamani and Mohamed I. Eldesouki
Additional contact information
Anwer Mustafa Hilal: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj 16278, Saudi Arabia
Dalia H. Elkamchouchi: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Saud S. Alotaibi: Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Mecca 24382, Saudi Arabia
Mohammed Maray: Department of Information Systems, College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia
Mahmoud Othman: Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, Egypt
Amgad Atta Abdelmageed: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj 16278, Saudi Arabia
Abu Sarwar Zamani: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj 16278, Saudi Arabia
Mohamed I. Eldesouki: Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj 16278, Saudi Arabia

Sustainability, 2022, vol. 14, issue 21, 1-18

Abstract: Recently, facial expression-based emotion recognition techniques obtained excellent outcomes in several real-time applications such as healthcare, surveillance, etc. Machine-learning (ML) and deep-learning (DL) approaches can be widely employed for facial image analysis and emotion recognition problems. Therefore, this study develops a Transfer Learning Driven Facial Emotion Recognition for Advanced Driver Assistance System (TLDFER-ADAS) technique. The TLDFER-ADAS technique helps proper driving and determines the different types of drivers’ emotions. The TLDFER-ADAS technique initially performs contrast enhancement procedures to enhance image quality. In the TLDFER-ADAS technique, the Xception model was applied to derive feature vectors. For driver emotion classification, manta ray foraging optimization (MRFO) with the quantum dot neural network (QDNN) model was exploited in this work. The experimental result analysis of the TLDFER-ADAS technique was performed on FER-2013 and CK+ datasets. The comparison study demonstrated the promising performance of the proposed model, with maximum accuracy of 99.31% and 99.29% on FER-2013 and CK+ datasets, respectively.

Keywords: transfer learning; metaheuristics; facial emotion recognition; driver assistance system; smart cities (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/21/14308/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/21/14308/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:21:p:14308-:d:960762

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().