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Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples.

Yuan Gao, Jiayi Ma, Alan L Yuille

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 1, 2017
    PubMed
    Summary
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    This study introduces a semi-supervised sparse representation method for face recognition with limited data. The approach effectively handles linear and non-linear variations, significantly improving recognition accuracy even with a single labeled sample per person.

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Pattern Recognition

    Background:

    • Face recognition systems struggle with limited labeled data and variations like lighting and expressions.
    • Existing methods face challenges in mitigating nuisance variables in low-data scenarios.

    Purpose of the Study:

    • To develop a robust face recognition method for scenarios with few or single labeled examples.
    • To address both linear and non-linear nuisance variables in face recognition.

    Main Methods:

    • Proposed a semi-supervised sparse representation-based classification (SS-SRC) method.
    • Utilized a gallery dictionary and a variation dictionary to model face variations.
    • Employed a Gaussian mixture model for estimating prototype faces in a semi-supervised manner.

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    Main Results:

    • Demonstrated significantly improved performance in face recognition with insufficient labeled samples.
    • Achieved strong results even when only one labeled sample per person was available.
    • Validated the method on benchmark datasets: AR, Multi-PIE, CAS-PEAL, and LFW.

    Conclusions:

    • The proposed semi-supervised sparse representation method offers a robust solution for face recognition under data scarcity.
    • The approach effectively handles complex nuisance variables, outperforming existing methods.
    • This work advances the field of face recognition, particularly in practical, low-data scenarios.