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A novel local pattern descriptor--local vector pattern in high-order derivative space for face recognition.

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    A new Local Vector Pattern (LVP) descriptor in high-order derivative space enhances face recognition. This novel method outperforms existing descriptors like LBP, LDP, and LTrP on benchmark datasets.

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    Area of Science:

    • Computer Vision
    • Pattern Recognition
    • Biometrics

    Background:

    • Face recognition systems rely on effective local pattern descriptors.
    • Existing methods like LBP, LDP, and LTrP have limitations in capturing complex micropatterns.

    Purpose of the Study:

    • To introduce a novel Local Vector Pattern (LVP) descriptor for improved face recognition.
    • To enhance feature extraction by utilizing high-order derivative spaces.

    Main Methods:

    • Proposed a Local Vector Pattern (LVP) descriptor based on pixel vectors in high-order derivative spaces.
    • Reduced feature length and encoded spatial relationships using pairwise vector directions.
    • Refined LVP by varying derivative orders for resilient micropattern representation.

    Main Results:

    • The proposed LVP descriptor demonstrated superior performance compared to LBP, LDP, and LTrP.
    • Experiments on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and LFW datasets validated LVP's effectiveness.
    • LVP achieved higher accuracy in face recognition tasks using grayscale images.

    Conclusions:

    • The novel LVP descriptor in high-order derivative space offers a significant advancement in face recognition.
    • LVP provides a more robust and discriminative feature representation for challenging face datasets.