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Face verification with balanced thresholds.

Shuicheng Yan1, Dong Xu, Xiaoou Tang

  • 1Microsoft Research Asia, Beijing 100080, China. scyan@ie.cuhk.edu.hk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 8, 2007
PubMed
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This study introduces Threshold Balanced Transformation (TBT), a novel algorithm for face verification that optimizes class-specific thresholds. TBT improves accuracy by balancing thresholds during model learning, outperforming existing methods.

Area of Science:

  • Computer Science
  • Machine Learning
  • Biometrics

Background:

  • Face verification often uses a global threshold, which is suboptimal for individual classes.
  • Existing methods may not adequately balance class-specific thresholds during model training.

Purpose of the Study:

  • To develop a new dimensionality reduction algorithm for face verification that ensures threshold balance.
  • To address the limitations of global thresholds in face verification systems.

Main Methods:

  • Proposed Threshold Balanced Transformation (TBT) algorithm using an affine transformation matrix.
  • Optimized the affine transformation matrix (product of orthogonal and diagonal matrices) iteratively.
  • Specifically designed TBT for face verification, distinguishing it from face identification techniques.

Related Experiment Videos

Main Results:

  • TBT ensures threshold balance, leading to improved face verification performance.
  • Experiments on benchmark databases showed TBT significantly outperforms state-of-the-art subspace techniques.
  • Demonstrated the effectiveness of TBT in enhancing classification capability.

Conclusions:

  • TBT is a specialized and effective algorithm for face verification.
  • The proposed method offers a significant improvement over existing subspace techniques.
  • Balancing class-specific thresholds is crucial for optimal face verification performance.