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This study enhances Sparse Representation based Classification (SRC) for face recognition by using Robust PCA and Singular Value Decomposition to extract eigenfaces, improving accuracy with noisy images.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Face recognition systems often struggle with variations in illumination and occlusions.
  • Sparse Representation based Classification (SRC) is a prominent technique but can be sensitive to image noise.

Purpose of the Study:

  • To improve the robustness and effectiveness of Sparse Representation based Classification (SRC) for face recognition.
  • To develop a method that mitigates the impact of noise on face image data.

Main Methods:

  • Low-rank representation using Robust Principal Component Analysis (Robust PCA) to extract essential features from face images.
  • Eigenface extraction via Singular Value Decomposition (SVD) from the low-rank images.
  • Construction of a compact and discriminative dictionary using extracted eigenfaces for sparse representation.

Main Results:

  • The proposed method demonstrates improved performance in face recognition tasks.
  • Experimental results on five popular databases confirm the effectiveness and robustness of the approach.
  • The technique successfully alleviates the influence of noise such as illumination differences and occlusions.

Conclusions:

  • The integration of Robust PCA and SVD for eigenface extraction offers a significant enhancement to SRC.
  • The developed method provides a more reliable face recognition solution in the presence of image variations.
  • The approach is validated across multiple benchmark datasets, indicating broad applicability.