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Deep Learning-based Open-set Person Identification using Radar Extracted Cardiac Signals.

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    This study presents a new deep learning algorithm for radar-based person identification using vital signs. It achieves high accuracy in both close-set and open-set conditions, outperforming existing methods.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Non-contact vital sign measurement using radar is gaining traction.
    • Existing person identification methods often struggle with open-set conditions where test data includes unseen categories.

    Purpose of the Study:

    • To develop a novel deep learning algorithm for person identification using radar-extracted vital signs.
    • To address the challenges of open-set person identification in real-world scenarios.

    Main Methods:

    • Extracting heart pulse signals from Doppler radar echoes.
    • Employing transfer learning with two Convolutional Neural Network (CNN) models.
    • Utilizing a distribution model for calibration and a strategic decision-making process for combining model outputs.

    Main Results:

    • Achieved 99.61% identification accuracy in close-set conditions.
    • Demonstrated 94.35% identification accuracy in challenging open-set conditions.
    • Outperformed existing person identification approaches.

    Conclusions:

    • The proposed deep learning algorithm offers a robust solution for radar-based person identification.
    • The method shows significant promise for real-world applications, particularly in open-set scenarios.