Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm

Abstract Facial expression recognition (FER) has advanced applications in various disciplines, including computer vision, Internet of Things, and artificial intelligence, supporting diverse domains such as medical escort services, learning analysis, fatigue detection, and human-computer interaction....

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Main Authors: Mu Panliang, Sanjay Madaan, Siddiq Ahmed Babikir Ali, Gowrishankar J., Ali Khatibi, Anas Ratib Alsoud, Vikas Mittal, Lalit Kumar, A. Johnson Santhosh
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-85206-9
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author Mu Panliang
Sanjay Madaan
Siddiq Ahmed Babikir Ali
Gowrishankar J.
Ali Khatibi
Anas Ratib Alsoud
Vikas Mittal
Lalit Kumar
A. Johnson Santhosh
author_facet Mu Panliang
Sanjay Madaan
Siddiq Ahmed Babikir Ali
Gowrishankar J.
Ali Khatibi
Anas Ratib Alsoud
Vikas Mittal
Lalit Kumar
A. Johnson Santhosh
author_sort Mu Panliang
collection DOAJ
description Abstract Facial expression recognition (FER) has advanced applications in various disciplines, including computer vision, Internet of Things, and artificial intelligence, supporting diverse domains such as medical escort services, learning analysis, fatigue detection, and human-computer interaction. The accuracy of these systems is of utmost concern and depends on effective feature selection, which directly impacts their ability to accurately detect facial expressions across various poses. This research proposes a new hybrid approach called QIFABC (Hybrid Quantum-Inspired Firefly and Artificial Bee Colony Algorithm), which combines the Quantum-Inspired Firefly Algorithm (QIFA) with the Artificial Bee Colony (ABC) method to enhance feature selection for a multi-pose facial expression recognition system. The proposed algorithm uses the attributes of both the QIFA and ABC algorithms to enhance search space exploration, thereby improving the robustness of features in FER. The firefly agents initially move toward the brightest firefly until identified, then search transition to the ABC algorithm, targeting positions with the highest nectar quality. In order to evaluate the efficacy of the proposed QIFABC algorithm, feature selection is also conducted using QIFA, FA, and ABC algorithms. The evaluated features are utilized for classifying face expressions by utilizing the deep neural network model, ResNet-50. The presented FER system has been tested using multi-pose facial expression benchmark datasets, including RaF (Radboud Faces) and KDEF (Karolinska Directed Emotional Faces). Experimental results show that the proposed QIFABC with ResNet50 method achieves an accuracy of 98.93%, 94.11%, and 91.79% for front, diagonal, and profile poses on the RaF dataset, respectively, and 98.47%, 93.88%, and 91.58% on the KDEF dataset.
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spelling doaj-art-881e7ea3a54d40a7a3a11b5d27549b862025-02-09T12:28:21ZengNature PortfolioScientific Reports2045-23222025-02-0115112310.1038/s41598-025-85206-9Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithmMu Panliang0Sanjay Madaan1Siddiq Ahmed Babikir Ali2Gowrishankar J.3Ali Khatibi4Anas Ratib Alsoud5Vikas Mittal6Lalit Kumar7A. Johnson Santhosh8National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan UniversityDepartment of Computer Engineering and Applications, GLA UniversityCollege of Administrative Sciences, Applied Science UniversityDepartment of Computer Science Engineering, School of Engineering and Technology, JAIN (Deemed to be University)Management and Science UniversityTECH, Harvard John A. Paulson School of Engineering & Applied Sciences, Harvard UniversityDepartment of Electronics and Communication Engineering, Chandigarh UniversityDepartment of Industry, Shri Vishwakarma Skill UniversityFaculty of Mechanical Engineering, Jimma Institute of Technology, Jimma UniversityAbstract Facial expression recognition (FER) has advanced applications in various disciplines, including computer vision, Internet of Things, and artificial intelligence, supporting diverse domains such as medical escort services, learning analysis, fatigue detection, and human-computer interaction. The accuracy of these systems is of utmost concern and depends on effective feature selection, which directly impacts their ability to accurately detect facial expressions across various poses. This research proposes a new hybrid approach called QIFABC (Hybrid Quantum-Inspired Firefly and Artificial Bee Colony Algorithm), which combines the Quantum-Inspired Firefly Algorithm (QIFA) with the Artificial Bee Colony (ABC) method to enhance feature selection for a multi-pose facial expression recognition system. The proposed algorithm uses the attributes of both the QIFA and ABC algorithms to enhance search space exploration, thereby improving the robustness of features in FER. The firefly agents initially move toward the brightest firefly until identified, then search transition to the ABC algorithm, targeting positions with the highest nectar quality. In order to evaluate the efficacy of the proposed QIFABC algorithm, feature selection is also conducted using QIFA, FA, and ABC algorithms. The evaluated features are utilized for classifying face expressions by utilizing the deep neural network model, ResNet-50. The presented FER system has been tested using multi-pose facial expression benchmark datasets, including RaF (Radboud Faces) and KDEF (Karolinska Directed Emotional Faces). Experimental results show that the proposed QIFABC with ResNet50 method achieves an accuracy of 98.93%, 94.11%, and 91.79% for front, diagonal, and profile poses on the RaF dataset, respectively, and 98.47%, 93.88%, and 91.58% on the KDEF dataset.https://doi.org/10.1038/s41598-025-85206-9Artificial Bee colonyDeep learningFacial expression recognitionFeature selectionFirefly AlgorithmMetaheuristic
spellingShingle Mu Panliang
Sanjay Madaan
Siddiq Ahmed Babikir Ali
Gowrishankar J.
Ali Khatibi
Anas Ratib Alsoud
Vikas Mittal
Lalit Kumar
A. Johnson Santhosh
Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm
Scientific Reports
Artificial Bee colony
Deep learning
Facial expression recognition
Feature selection
Firefly Algorithm
Metaheuristic
title Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm
title_full Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm
title_fullStr Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm
title_full_unstemmed Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm
title_short Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm
title_sort enhancing feature selection for multi pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm
topic Artificial Bee colony
Deep learning
Facial expression recognition
Feature selection
Firefly Algorithm
Metaheuristic
url https://doi.org/10.1038/s41598-025-85206-9
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