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Manta Ray Foraging Optimization with Transfer Learning Driven Facial Emotion Recognition

Author

Listed:
  • 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)

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.

Suggested Citation

  • Anwer Mustafa Hilal & Dalia H. Elkamchouchi & Saud S. Alotaibi & Mohammed Maray & Mahmoud Othman & Amgad Atta Abdelmageed & Abu Sarwar Zamani & Mohamed I. Eldesouki, 2022. "Manta Ray Foraging Optimization with Transfer Learning Driven Facial Emotion Recognition," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14308-:d:960762
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    References listed on IDEAS

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    1. Suparshya Babu Sukhavasi & Susrutha Babu Sukhavasi & Khaled Elleithy & Ahmed El-Sayed & Abdelrahman Elleithy, 2022. "A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach," IJERPH, MDPI, vol. 19(5), pages 1-19, March.
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    Cited by:

    1. Zahra Ahanin & Maizatul Akmar Ismail & Narinderjit Singh Sawaran Singh & Ammar AL-Ashmori, 2023. "Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages," Sustainability, MDPI, vol. 15(16), pages 1-24, August.

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