Quantum-inspired vortex search algorithm with deep neural networks for multi-pose facial expression recognition

Abhishek Bhatt (), Rama Kant (), Monica Luthra (), Sonika Jindal (), Thejo Lakshmi Gudipalli (), Vishal Jain () and Meenu
Additional contact information
Abhishek Bhatt: School of Data Science, Symbiosis Skills and Professional University, Pune 412101, Maharashtra, India
Rama Kant: Department of Computer Science and Engineering, GL Bajaj Group of Institutions, Mathura 281406, Uttar Pradesh, India
Monica Luthra: Department of AIT-CSE, Chandigarh University, Gharuan 140413, Punjab, India
Sonika Jindal: Department of Computer Science and Engineering, Shaheed Bhagat Singh State University, Ferozepur 152001, Punjab, India
Thejo Lakshmi Gudipalli: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram 522302, Andhra Pradesh, India
Vishal Jain: Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida 201310, Uttar Pradesh, India
Meenu: Department of Computer Science, Institute of Innovation in Technology and Management, Janakpuri, Delhi 110058, India

International Journal of Modern Physics C (IJMPC), 2024, vol. 35, issue 12, 1-26

Abstract: The rapid expansion of artificial intelligence technologies has enabled machines to comprehend emotional intelligence. Among various indicators, facial expressions serve as an effective medium for understanding emotions. The concept of facial expression recognition (FER) relies heavily on the accurate and robust features available. Initially, the method of three-channel convolutional neural networks (TC-CNN) is adapted to extract facial features. However, only extracting the features is insufficient, the optimization of the extracted features is crucial to determining precise and robust features. This research work focuses on the optimization of the features using the quantum-inspired vortex search algorithm (QVSA). The QVSA integrates the attributes of Q-bits into the vortex search algorithm (VSA), optimizing the features by using the Q-bits to determine the vortex center on the Bloch sphere. The Q-bit attributes also improve the diversity of the features and help to avoid the premature convergence of the VSA. The final recognition of the facial expressions is performed using the deep neural network method of ResNet101v2. The experiments for facial expression recognition are performed on the datasets of RaFD and KDEF, which include different facial positions such as front pose, diagonal pose and profile pose. Performance comparisons demonstrate the effectiveness of the proposed system over state-of-the-art facial expression techniques.

Keywords: Deep learning; deep neural networks; facial expression recognition; meta-heuristic algorithm; multi-pose expressions; Q-bits; quantum-inspired computation; quantum-inspired vortex search algorithm; vortex search algorithm (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0129183124501535
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:35:y:2024:i:12:n:s0129183124501535

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0129183124501535

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 ().