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Single-sample face recognition based on intra-class differences in a variation model.

Jun Cai1, Jing Chen2, Xing Liang3

  • 1School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China. purepurple@bit.edu.cn.

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Summary
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This study introduces a new random facial variation model to improve sparse representation face recognition, especially for single-sample recognition challenges. The novel method enhances accuracy when training data is limited.

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Area of Science:

  • Computer Science
  • Biometrics
  • Machine Learning

Background:

  • Sparse Representation-Based Classification (SRC) is effective for face recognition but requires extensive training data.
  • Single-sample face recognition remains a significant challenge due to limited available data per individual.

Purpose of the Study:

  • To develop a novel random facial variation modeling system to overcome the data limitations of SRC.
  • To address the critical problem of insufficient training samples in sparse representation face recognition.

Main Methods:

  • A facial variation modeling system based on random projection and sparse representation is proposed.
  • A novel facial random noise dictionary learning method, invariant to different faces, is introduced.

Main Results:

  • The proposed method significantly improves face recognition performance, particularly in single-sample scenarios.
  • Experiments on multiple benchmark databases (AR, Yale B, Extended Yale B, MIT, FEI) validate the effectiveness.

Conclusions:

  • The novel random facial variation modeling system effectively addresses the limitations of traditional SRC.
  • The method offers a substantial improvement for single-sample face recognition, enhancing robustness and accuracy.