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Face recognition using laplacianfaces.

Xiaofei He, Shuicheng Yan, Yuxiao Hu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 8, 2005
    PubMed
    Summary
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    The new Laplacianface approach enhances face recognition by preserving local facial structure. This method reduces variations from lighting and pose, achieving lower error rates than Eigenface and Fisherface.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Pattern Recognition

    Background:

    • Appearance-based face recognition methods like PCA and LDA focus on Euclidean structure.
    • These methods can be sensitive to variations in lighting, expression, and pose.
    • A method preserving local manifold structure is needed for robust face recognition.

    Purpose of the Study:

    • To propose the Laplacianface approach for appearance-based face recognition.
    • To leverage Locality Preserving Projections (LPP) for enhanced face subspace analysis.
    • To reduce variations from lighting, expression, and pose for improved recognition accuracy.

    Main Methods:

    • Utilized Locality Preserving Projections (LPP) to map face images into a subspace.
    • Developed Laplacianfaces as optimal linear approximations to eigenfunctions of the Laplace Beltrami operator.

    Related Experiment Videos

  • Compared Laplacianface with Eigenface and Fisherface methods on three face datasets.

    • Main Results:

    • Laplacianface effectively captures the essential face manifold structure by preserving local information.
    • The method demonstrated a reduction in unwanted variations due to lighting, expression, and pose.
    • Experimental results showed that Laplacianface provides better representation and lower error rates compared to Eigenface and Fisherface.

    Conclusions:

    • The Laplacianface approach offers a superior method for appearance-based face recognition.
    • Preserving local information and manifold structure leads to more robust face recognition.
    • Laplacianface outperforms traditional methods like Eigenface and Fisherface in accuracy.