IDEAS home Printed from https://ideas.repec.org/a/jbo/ijsrml/v2y2026i5id111.html

Robust Deep Learning-Based Face Recognition Under Unseen Combinations of Real-World Image Distortions

Author

Listed:
  • Ms
  • Pradarsha Kolapudi
  • Dr
  • Sheshang Degadwala
  • Ms
  • Malini Joshi

Abstract

While more recent work evaluates larger OOD situations and realistic degradations, deep face recognition can suffer when capture conditions deviate from training data; blur, noise, and occlusion have negatively impacted performance in face-recognition evaluations.[1-3] Although staged or adaptive training and distortion augmentation are viable approaches, the research addressed here does not prove generalization to systematically held-out combinations of natural distortions.[3, 4] This paper makes the case that robustness evaluation should report task-appropriate, subgroup, and clean-image performance; differentiate distortion types, severities, and combinations; and use naturally acquired data. The main finding is that deployment robustness cannot be fully demonstrated by single-corruption outcomes.

Suggested Citation

  • Ms & Pradarsha Kolapudi & Dr & Sheshang Degadwala & Ms & Malini Joshi, 2026. "Robust Deep Learning-Based Face Recognition Under Unseen Combinations of Real-World Image Distortions," International Journal of Scientific Research in Artificial Intelligence and Machine Learning, International Journal of Scientific Research in Artificial Intelligence and Machine Learning, vol. 2(5), pages 163-173, September.
    • Handle: RePEc:jbo:ijsrml:v2:y2026:i5:id:111
    DOI: 10.32628/IJSRAIML2624120
    Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML2624120
    as

    Download full text from publisher

    File URL: https://ijsraiml.com/home/article/view/IJSRAIML2624120
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsraiml.com/home/article/download/IJSRAIML2624120/IJSRAIML2624120
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/IJSRAIML2624120?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
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jbo:ijsrml:v2:y2026:i5:id:111. 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: Pankaj Sharma (email available below). General contact details of provider: https://ijsraiml.com/home .

    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.