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Related Experiment Videos

Formulating face verification with semidefinite programming.

Shuicheng Yan1, Jianzhuang Liu, Xiaoou Tang

  • 1School of Computer Engineering, Nanyang Technological University, Singapore 639798. shuicheng.yan@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 10, 2007
PubMed
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This study introduces the Affine Subspace for Verification (ASV), a novel method for face verification. ASV effectively addresses key challenges in subspace learning, improving accuracy without parameter tuning.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Face verification relies on subspace learning techniques, but faces challenges in threshold selection, subspace dimension determination, and feature fusion.
  • Existing algorithms often directly search for projection matrices, limiting their effectiveness.

Purpose of the Study:

  • To present a unified solution for unsolved problems in face verification using subspace learning.
  • To introduce a new algorithm, Affine Subspace for Verification (ASV), that overcomes limitations of previous methods.

Main Methods:

  • Investigated a similarity metric matrix (SMM) learned via semidefinite programming.
  • Simultaneously inferred subspace dimension and feature fusion weights from SMM's singular value decomposition.

Related Experiment Videos

  • Proposed weighted and tensor extensions for enhanced effectiveness and efficiency.

    • Main Results:

    • ASV achieves encouraging face verification accuracy compared to other subspace algorithms.
    • The method functions effectively without requiring parameter exploration.
    • Demonstrated improved algorithmic effectiveness and efficiency through extensions.

    Conclusions:

    • ASV offers a robust and parameter-free approach to face verification.
    • The unified solution addresses critical issues in current subspace learning techniques.
    • The developed method shows significant potential for practical face recognition systems.