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Weighted Extreme Sparse Classifier and Local Derivative Pattern for 3D Face Recognition.

Sima Soltanpour, Qing Ming Jonathan Wu

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    This study introduces a new 3D face recognition method using local derivative patterns and a hybrid classifier. The approach enhances accuracy and speed for 3D facial recognition systems.

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    Area of Science:

    • Computer Science
    • Biometrics
    • Pattern Recognition

    Background:

    • 3D face recognition is crucial for security and identification.
    • Existing methods face challenges with accuracy and computational cost.
    • Local derivative patterns (LDP) offer detailed shape information.

    Purpose of the Study:

    • To propose a novel weighted hybrid classifier for 3D face recognition.
    • To introduce a high-order local normal derivative pattern descriptor.
    • To improve recognition accuracy and computational efficiency.

    Main Methods:

    • Utilizing nth-order Local Derivative Patterns (LDP) on surface normals for feature extraction.
    • Employing an Extreme Learning Machine (ELM) autoencoder for feature selection and fast training.
    • Implementing a weighted hybrid classifier combining ELM and Sparse Representation Classifier (SRC).

    Main Results:

    • The proposed method demonstrates effectiveness across four 3D face databases.
    • Achieved enhanced recognition accuracy compared to existing methods.
    • Showcased improved computational cost and recognition performance.

    Conclusions:

    • The novel weighted hybrid classifier and LDP descriptor significantly advance 3D face recognition.
    • The method effectively handles facial variations and challenges.
    • Offers a robust and efficient solution for 3D facial identification systems.