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Local Sliced Wasserstein Feature Sets for Illumination Invariant Face Recognition.

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This study introduces a novel face recognition method using the Radon Cumulative Distribution Transform (R-CDT) to model illumination variations. The approach effectively recognizes faces under challenging lighting conditions, outperforming existing methods.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Face recognition systems often struggle with variations in illumination.
  • Existing methods may not adequately address complex lighting deformations in facial images.

Purpose of the Study:

  • To develop a robust face recognition method resilient to varying illumination conditions.
  • To model and compensate for illumination-induced deformations in local image gradients.

Main Methods:

  • Utilizing the Radon Cumulative Distribution Transform (R-CDT) for mathematical modeling of local gradient distributions.
  • Representing illumination-induced deformations as a subspace in the R-CDT domain.
  • Employing a nearest subspace method for face recognition in the R-CDT domain.

Main Results:

  • The proposed R-CDT-based method demonstrates superior performance in face recognition tasks with challenging illumination.
  • Experimental results confirm the effectiveness of modeling gradient distribution deformations as a subspace.
  • The method significantly outperforms alternative approaches under varied lighting.

Conclusions:

  • The R-CDT-based approach offers a robust solution for face recognition under difficult illumination.
  • The mathematical modeling of gradient distributions provides a powerful framework for handling illumination variations.
  • Publicly available Python code (PyTransKit) facilitates the implementation and adoption of this method.