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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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An Efficient Joint Formulation for Bayesian Face Verification.

Dong Chen, Xudong Cao, David Wipf

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |February 26, 2016
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    Summary
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    This study enhances Bayesian face recognition for verification by jointly modeling image pairs, improving accuracy and efficiency. The new method outperforms classical and state-of-the-art algorithms on challenging datasets.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Biometrics

    Background:

    • Classical Bayesian face recognition models often rely on appearance differences, potentially limiting class separability.
    • Existing face verification algorithms may not optimally handle intra- and extra-personal variations in facial images.

    Purpose of the Study:

    • To enhance the classical Bayesian face recognition algorithm for improved face verification performance.
    • To address limitations in existing models by jointly considering facial image pairs and their variations.

    Main Methods:

    • A novel joint formulation models two facial images with a prior that accounts for intra- and extra-personal variations.
    • The model is trained using an Expectation-Maximization (EM) algorithm.
    • Testing utilizes efficient closed-form computations for real-time deployment.

    Main Results:

    • The proposed model demonstrates superior performance compared to the classical Bayesian face algorithm.
    • Achieved state-of-the-art test accuracy on Labeled Face in the Wild, Multi-PIE, and YouTube Faces datasets.
    • The method offers unparalleled computational efficiency for practical applications.

    Conclusions:

    • The joint modeling approach significantly improves face verification accuracy and computational efficiency.
    • The enhanced Bayesian face recognition model offers a robust and scalable solution for real-world biometric systems.