A Novel Domain Adaptation-based Framework for Face Recognition Under Darkened and Overexposed Situations

Authors

  • Jiahuai Ma

    University of Florida, Gainesville, FL 32603, USA
  • Alan Wilson

    Intact Financial Corporation, Montréal, Québec H3A 2A5, Canada

DOI:

https://doi.org/10.30564/aia.v5i1.8691
Received: 5 February 2023 | Revised: 21 March 2023 | Accepted: 25 March 2023 | Published Online: 15 December 2023

Abstract

Face recognition has become a cornerstone technology in various domains, including security, healthcare, and personalized applications. While traditional methods relied on handcrafted features and classical machine learning, advancements in deep learning have significantly improved face recognition's accuracy and robustness. However, challenges such as environmental variations—darkened or overexposed images—create domain shifts that compromise the generalization of these models. To address this, domain adaptation techniques have emerged as a promising solution, aligning feature distributions between source domain and target domain. This paper proposes a domain adaptation framework integrating Correlation Alignment (CORAL) and a Residual Network (ResNet) to enhance model robustness under varying conditions. Our method effectively reduces domain discrepancies using CORAL loss. Experimental results demonstrate that domain adaptation significantly improves model performance, as evidenced by reduced Equal Error Rates (EER) and enhanced feature alignment in challenging lighting scenarios. Despite its success, domain adaptation faces challenges such as computational costs and handling extreme distortions, highlighting the need for further research into more efficient and generalized approaches.

Keywords:

Face recognition; Deep learning; Domain adaptation

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How to Cite

Jiahuai Ma, & Wilson, A. (2023). A Novel Domain Adaptation-based Framework for Face Recognition Under Darkened and Overexposed Situations. Artificial Intelligence Advances, 5(1), 63–. https://doi.org/10.30564/aia.v5i1.8691

Issue

Vol. 5 , Iss. 1 (April 2023):

Article Type

Article

License

Copyright © 2023 Jiahuai Ma, Alan Wilson

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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