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Feature Selection for Nonstationary Data: Application to Human Recognition Using Medical Biometrics.

Majid Komeili, Wael Louis, Narges Armanfard

    IEEE Transactions on Cybernetics
    |May 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new feature selection method for robust human recognition using physiological signals like electrocardiogram (ECG) and transient evoked otoacoustic emissions (TEOAE). The method enhances cross-session identification accuracy by identifying persistent features.

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    Area of Science:

    • Biometrics
    • Signal Processing
    • Machine Learning

    Background:

    • Electrocardiogram (ECG) and transient evoked otoacoustic emissions (TEOAE) are robust physiological signals for biometrics.
    • Time-dependent nature of these signals poses challenges for across-session recognition.
    • Existing methods struggle with enrollment limitations (single session).

    Purpose of the Study:

    • To develop a novel feature selection method for across-session human recognition.
    • To address the challenge of time-dependent physiological signals in biometrics.
    • To improve the robustness of biometric systems using ECG and TEOAE.

    Main Methods:

    • A novel feature selection method utilizing an auxiliary dataset with multiple sessions.
    • Selection of features exhibiting persistence across different sessions.
    • Incorporation of local sample margins and an across-session measure.

    Main Results:

    • Comprehensive experiments evaluated ECG and TEOAE variability due to time lapse and body posture.
    • The proposed method demonstrated superior performance compared to seven state-of-the-art feature selection algorithms.
    • Outperformed six other established ECG and TEOAE biometric recognition approaches.

    Conclusions:

    • The proposed feature selection method effectively handles time-dependent physiological signals for biometrics.
    • It offers a significant improvement for across-session human recognition scenarios.
    • The method is robust and suitable for real-world biometric applications using ECG and TEOAE.