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Generalized discriminant orthogonal nonnegative tensor factorization for facial expression recognition.

Zhang XiuJun1, Liu Chang1

  • 1College of Information Science and Technology, Chengdu University, Chengdu 610106, China ; Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan, Chengdu 610106, China.

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This study introduces a novel generalized discriminant orthogonal non-negative tensor factorization algorithm. It enhances feature discrimination for improved facial expression recognition compared to traditional methods.

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Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Traditional non-negative factorization algorithms have limitations.
  • Feature extraction is crucial for pattern recognition tasks like facial expression recognition.

Purpose of the Study:

  • To present a generalized discriminant orthogonal non-negative tensor factorization algorithm.
  • To overcome limitations of existing non-negative factorization methods.
  • To improve the discriminant capability of low-dimensional features.

Main Methods:

  • Incorporated orthogonal constraints to ensure nonnegativity of low-dimensional features.
  • Imposed discriminant constraints on low-dimensional weights.
  • Evaluated the algorithm on facial expression recognition tasks.

Main Results:

  • The proposed algorithm ensures nonnegativity and enhances feature discrimination.
  • Experimental results demonstrate superior performance compared to other non-negative factorization algorithms.
  • Effective for facial expression recognition.

Conclusions:

  • The generalized discriminant orthogonal non-negative tensor factorization algorithm is effective.
  • The algorithm offers advantages over traditional methods for feature extraction and recognition.
  • Validated through successful application in facial expression recognition.