IDEAS home Printed from https://ideas.repec.org/a/ids/injdan/v16y2024i3p262-281.html
   My bibliography  Save this article

CEVAB: NIR-VIS face recognition using convolutional encoder-based visual attention block

Author

Listed:
  • Patil Jayashree Madhukar
  • P.M. Ashok Kumar
  • R. Anitha

Abstract

Recent research in night vision face recognition has spiked due to the rise of night-time surveillance in public areas, where cameras often use near infrared (NIR) images. This paper presents a new face recognition method, the convolutional encoder-based visual attention block (CEVAB), optimised for NIR and visible spectrum (VIS) images. CEVAB combines a convolutional encoder with an attention-based architecture, focusing on critical facial features to enhance accuracy against watchlists. Tested on the FaceSurv dataset with over 132,000 images, CEVAB outshines traditional methods in VIS, achieving 95.08% Rank 1 accuracy at close distances, and in NIR, with 74.00% Rank 1 accuracy, surpassing competitors like Verilook and ResNet-50. These results prove CEVAB's exceptional adaptability and performance in various imaging conditions, significantly advancing night vision face recognition technology.

Suggested Citation

  • Patil Jayashree Madhukar & P.M. Ashok Kumar & R. Anitha, 2024. "CEVAB: NIR-VIS face recognition using convolutional encoder-based visual attention block," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 16(3), pages 262-281.
    • Handle: RePEc:ids:injdan:v:16:y:2024:i:3:p:262-281
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=140650
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:injdan:v:16:y:2024:i:3:p:262-281. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=282 .

    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.