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Extended SRC: undersampled face recognition via intraclass variant dictionary.

Weihong Deng1, Jiani Hu, Jun Guo

  • 1Beijing University of Posts and Telecommunications, Beijing, China. whdeng@bupt.edu.cn

IEEE Transactions on Pattern Analysis and Machine Intelligence
|July 21, 2012
PubMed
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Extended Sparse Representation-Based Classifier (ESRC) improves face recognition with limited training data. This method enhances Sparse Representation-Based Classification (SRC) for better generalization in challenging conditions.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Sparse Representation-Based Classification (SRC) is effective for face recognition with ample training data.
  • Undersampled face recognition, with few or single training images per subject, remains a challenge.

Purpose of the Study:

  • To extend SRC for face recognition scenarios with very limited training images per subject.
  • To improve the generalization ability of SRC algorithms for large-scale face recognition.

Main Methods:

  • Introduced Extended Sparse Representation-Based Classifier (ESRC).
  • ESRC utilizes an auxiliary intraclass variant dictionary to model variations between training and testing images.
  • Dictionary atoms represent intraclass sample differences from gallery or generic faces.

Related Experiment Videos

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08:27

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Published on: January 5, 2024

Main Results:

  • ESRC demonstrated superior generalization ability compared to SRC on AR and FERET databases.
  • ESRC performed effectively under variable expressions, illuminations, disguises, and ages.
  • The study suggests SRC algorithms can generalize to large-scale problems with proper dictionary construction, even with one training image per class.

Conclusions:

  • ESRC offers a robust solution for face recognition with limited training samples.
  • The proposed method significantly enhances the performance of SRC in undersampled conditions.
  • Proper dictionary construction is key for SRC algorithms to achieve broad applicability in face recognition.