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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Ordinal feature selection for iris and palmprint recognition.

Zhenan Sun, Libin Wang, Tieniu Tan

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 17, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for selecting ordinal features, improving accuracy in iris and palmprint recognition. The approach optimizes feature representation for better biometric identification.

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    Area of Science:

    • Computer Science
    • Biometrics
    • Machine Learning

    Background:

    • Ordinal measures are effective for iris and palmprint recognition.
    • A vast feature space arises from numerous ordinal measure variants.

    Purpose of the Study:

    • To propose a novel optimization formulation for ordinal feature selection.
    • To enhance accuracy and sparsity in biometric recognition.

    Main Methods:

    • Formulated feature selection as a linear programming (LP) problem.
    • Objective function includes misclassification error and weighted sparsity.
    • Optimization subject to linear inequality constraints for sample separation.

    Main Results:

    • Achieved accurate and sparse representation of ordinal measures.
    • Demonstrated state-of-the-art accuracy on CASIA and PolyU databases.
    • Outperformed existing methods like mRMR, ReliefF, Boosting, and Lasso.

    Conclusions:

    • The proposed LP formulation is efficient for large-scale biometric data.
    • This method offers significant advantages over traditional feature selection techniques.
    • Successfully applied to iris and palmprint recognition for improved performance.