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Multilinear discriminant analysis for face recognition.

Shuicheng Yan1, Dong Xu, Qiang Yang

  • 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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Multilinear discriminant analysis (MDA) offers a novel solution for face recognition by using higher-order tensors to overcome the curse of dimensionality. This subspace learning technique enhances accuracy, especially with limited data.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Subspace learning is crucial for face recognition but faces challenges with high-dimensional data, leading to the curse of dimensionality.
  • Traditional vector-based methods struggle with the complexity and scale of image data in face recognition tasks.

Purpose of the Study:

  • To introduce a novel supervised dimensionality reduction approach for face recognition using tensor representations.
  • To develop an algorithm, multilinear discriminant analysis (MDA), that addresses the curse of dimensionality and small sample size problems.

Main Methods:

  • Images are encoded as second- or higher-order tensors.
  • A discriminant tensor criterion is proposed for feature extraction.
  • K-mode optimization is used to iteratively learn multiple interrelated lower-dimensional subspaces by unfolding tensors.

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Main Results:

  • The proposed multilinear discriminant analysis (MDA) algorithm effectively reduces data dimensions, mitigating the curse of dimensionality.
  • MDA demonstrates superior performance compared to traditional vector-based subspace learning algorithms on benchmark face databases (ORL, CMU PIE, FERET).
  • The algorithm shows particular effectiveness in scenarios with small sample sizes.

Conclusions:

  • Multilinear discriminant analysis (MDA) provides a powerful framework for face recognition by leveraging higher-order tensor analysis.
  • MDA's ability to handle high-dimensional data and small sample sizes makes it a promising technique for advanced pattern recognition.