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Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
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An Analytic Gabor Feedforward Network for Single-Sample and Pose-Invariant Face Recognition.

Beom-Seok Oh, Kar-Ann Toh, Andrew Beng Jin Teoh

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 24, 2018
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    Summary

    This study introduces an analytic Gabor feedforward network to improve face recognition accuracy despite head pose variations. The novel network effectively absorbs moderate imaging changes, enhancing identification performance.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Biometrics

    Background:

    • Gabor magnitude features offer discriminative power for face recognition due to their space-frequency co-localization.
    • This property, however, leads to sensitivity to moderate head pose variations and other imaging inconsistencies.

    Purpose of the Study:

    • To develop an analytic Gabor feedforward network capable of mitigating pose sensitivity and other moderate variations in face images.
    • To enhance face identification accuracy and computational efficiency in the presence of imaging variations.

    Main Methods:

    • The proposed network processes raw face images directly, generating directionally projected Gabor magnitude features in the hidden layer.
    • Magnitude features from various orientations and scales are fused at the output layer for classification.
    • The network model is analytically trained using a single sample per identity, ensuring a globally optimal solution.

    Main Results:

    • Empirical experiments on five public face datasets (six subsets) demonstrated encouraging results.
    • The network achieved significant improvements in identification accuracy.
    • The proposed method also showed high computational efficiency.

    Conclusions:

    • The analytic Gabor feedforward network effectively addresses the pose sensitivity of Gabor magnitude features.
    • This approach offers a robust and efficient solution for face recognition under moderate imaging variations.
    • The globally optimal training strategy contributes to superior classification performance.