IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v15y2024i8d10.1007_s13198-024-02382-z.html
   My bibliography  Save this article

Deep dual domain joint discriminant feature framework for emotion based music player

Author

Listed:
  • A. Sasithradevi

    (Vellore Institute of Technology)

  • Ravi Teja Challa

    (Vellore Institute of Technology)

  • Siva Saketh

    (Vellore Institute of Technology)

  • Saketh Chakka

    (Vellore Institute of Technology)

  • D. Arumuga Perumal

    (National Institute of Technology Karnataka)

  • P. Prakash

    (Madras Institute of Technology)

Abstract

Emotion based music player is an interdisciplinary study of computer vision and psychology. As music enhances the positive vibes it plays a significant role in soothing people’s emotion. Emotions can be predicted through facial expression analysis using vision-based methods. However, challenges like environment and expression complexity have become hindrance to attain a good recognition rate. Therefore, we put forward a deep dual domain joint feature framework based on linear discriminant analysis for facial emotion recognition. First, we detect the human face and learn the emotion pattern using the popular complementary deep domain networks called EfficientNet and ResNet50. The learned deep dual domain space is projected onto linear discriminant space to achieve a joint discriminant feature space. The recognition rate of the proposed joint discriminant feature framework is analyzed using support vector machine. To prove the efficacy of the proposed framework, we validated it on two Benchmarks namely FER2013 and CK48+ datasets. The proposed framework achieved a good recognition rate of 99% and 98.6% on FER2013 and CK48+ respectively. Experimental analysis on our EmDe dataset showed an accuracy of 99% and proves that the deep dual domain joint discriminant framework as a promising pipeline for emotion-based music player system.

Suggested Citation

  • A. Sasithradevi & Ravi Teja Challa & Siva Saketh & Saketh Chakka & D. Arumuga Perumal & P. Prakash, 2024. "Deep dual domain joint discriminant feature framework for emotion based music player," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 3854-3868, August.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:8:d:10.1007_s13198-024-02382-z
    DOI: 10.1007/s13198-024-02382-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-024-02382-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-024-02382-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Naveen Kumari & Rekha Bhatia, 2023. "Deep learning based efficient emotion recognition technique for facial images," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1421-1436, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:15:y:2024:i:8:d:10.1007_s13198-024-02382-z. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.