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Related Experiment Videos

Incremental linear discriminant analysis for face recognition.

Haitao Zhao1, Pong Chi Yuen

  • 1Institute of Aerospace Science and Technology, Shanghai Jiao Tong University, Shanghai 200030, China. zhaoht@sjtu.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 14, 2008
PubMed
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This study introduces GSVD-ILDA, an incremental linear discriminant analysis (LDA) algorithm for scalable face recognition. It achieves comparable performance to existing methods with reduced computational complexity, improving classification accuracy.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Dimensionality reduction, particularly Linear Discriminant Analysis (LDA), is widely used in face recognition.
  • LDA-based systems face scalability challenges due to computational complexity.
  • Incremental learning offers a solution for handling large-scale datasets in face recognition.

Purpose of the Study:

  • To develop a novel incremental LDA (ILDA) algorithm addressing the scalability limitations of traditional LDA.
  • To propose GSVD-ILDA, an algorithm based on generalized singular value decomposition (GSVD) for efficient face recognition.
  • To determine the projection matrix in full space, unlike existing methods restricted to subspaces.

Main Methods:

  • Developed GSVD-ILDA, an incremental algorithm leveraging LDA/GSVD.

Related Experiment Videos

  • Implemented a method to handle the within-class scatter matrix inversion challenge in ILDA.
  • Conducted extensive experiments on benchmark face databases (e.g., Face Recognition Technology, CMU Pose, Illumination, and Expression).

    • Main Results:

    • GSVD-ILDA demonstrates performance equivalent to LDA/GSVD.
    • The proposed algorithm significantly reduces computational complexity compared to LDA/GSVD.
    • GSVD-ILDA outperforms other recently proposed ILDA algorithms in classification accuracy.

    Conclusions:

    • GSVD-ILDA offers an efficient and scalable solution for face recognition.
    • The algorithm effectively overcomes the scalability issues associated with traditional LDA.
    • GSVD-ILDA provides a promising advancement in incremental learning for pattern recognition tasks.