Quantum-inspired meta-heuristic algorithms with deep learning for facial expression recognition under varying yaw angles

Abhishek Bhatt (), Tanweer Alam (), Kantilal Pitambar Rane (), Rainu Nandal, Meenakshi Malik (), Rahul Neware () and Samiksha Goel ()
Additional contact information
Abhishek Bhatt: Department of Electronics and Telecommunication, College of Engineering Pune, Maharashtra-411005, India
Tanweer Alam: Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia
Kantilal Pitambar Rane: Department of Electronics and Telecom Engineering, KCE Society’s College of Engineering and Information Technology, Jalgaon, Maharashtra 425001, India
Rainu Nandal: Department of Computer Science and Engineering, U.I.E.T., Maharshi Dayanand University, Rohtak, Haryana 124001, India
Meenakshi Malik: Department of Computer Science and Engineering, Starex University, Gurugram, Haryana 122413, India
Rahul Neware: Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, Bergen, Norway
Samiksha Goel: Department of Computer Science, University of Delhi, Delhi 110007, India

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 04, 1-24

Abstract: In recent years, the increasing human–computer interaction has spurred the interest of researchers towards facial expression recognition to determine the expressive changes in human beings. The detection of relevant features that describe the expressions of different individuals is vital to describe human expressions accurately. The present work has employed the integrated concept of Local Binary Pattern and Histogram of Gradient for facial feature extraction. The major contribution of the paper is the optimization of the extracted features using quantum-inspired meta-heuristic algorithms of QGA (Quantum-Inspired Genetic Algorithm), QGSA (Quantum-Inspired Gravitational Search Algorithm), QPSO (Quantum-Inspired Particle Swarm Optimization), and QFA (Quantum-Inspired Firefly Algorithm). These quantum-inspired meta-heuristic algorithms utilize the attributes of quantum computing that ensure the adequate control of facial feature diversity with quantum measures and Q-bit superstition states. The optimized features are fed to the deep learning (DL) variant deep convolutional neural network added with residual blocks (DCNN-R) for the classification of expressions. The facial expressions are detected for the KDEF and RaFD datasets under varying yaw angles of –90∘, –45∘, 0∘, 45∘, and 90∘. The detection of facial expressions with varying angles is also a crucial contribution, as the features decrease with the increasing yaw angle movement of the face. The experimental evaluations demonstrate the superior performance of the QFA than other algorithms for feature optimization and hence the better classification of facial expressions.

Keywords: Quantum mechanics; quantum computing; meta-heuristic; genetic algorithm; deep learning; deep convolutional neural network; facial expression recognition (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0129183122500450
Access to full text is restricted to subscribers

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:wsi:ijmpcx:v:33:y:2022:i:04:n:s0129183122500450

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0129183122500450

Access Statistics for this article

International Journal of Modern Physics C (IJMPC) is currently edited by H. J. Herrmann

More articles in International Journal of Modern Physics C (IJMPC) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().