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PerAE: An Effective Personalized AutoEncoder for ECG-Based Biometric in Augmented Reality System.

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    |January 25, 2022
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    Summary
    This summary is machine-generated.

    This study introduces Personalized AutoEncoder (PerAE) for Electro-CardioGram (ECG)-based Identity Recognition (EIR). PerAE uses personalized autoencoder models to enhance security and efficiency in biometric systems, achieving high accuracy with minimal data.

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    Area of Science:

    • Biometrics and Human-Computer Interaction
    • Machine Learning and Artificial Intelligence
    • Cybersecurity and Privacy

    Background:

    • Augmented and Virtual Reality (AR/VR) technologies generate vast amounts of biometric data, increasing privacy risks.
    • Electro-CardioGram (ECG)-based Identity Recognition (EIR) offers a robust biometric solution due to the internal and time-continuous nature of ECG signals.
    • Traditional biometric methods like facial recognition are susceptible to various attacks, necessitating more secure alternatives.

    Purpose of the Study:

    • To propose and evaluate an novel Autoencoder-based EIR system, termed Personalized AutoEncoder (PerAE).
    • To enhance the security, efficiency, adaptability, scalability, and maintainability of ECG-based identity recognition systems.
    • To demonstrate the effectiveness of personalized autoencoder models in improving biometric recognition performance.

    Main Methods:

    • Development of a personalized autoencoder model, Attention-MemAE, for each registered user.
    • Integration of a memory module and two attention mechanisms within the Attention-MemAE to enhance anomaly detection.
    • Utilizing each user's Attention-MemAE to classify other users' heartbeats as anomalies, enabling personalized recognition.
    • Implementing an update mechanism for Attention-MemAE to adapt to changes in user ECG data distribution.

    Main Results:

    • The proposed PerAE system achieves high identification accuracy (90%) for a user.
    • Training an Attention-MemAE model requires minimal ECG data (around 500 heartbeat samples) and takes approximately five minutes.
    • PerAE demonstrates improved time efficiency and reduced memory overhead compared to traditional approaches.
    • The system exhibits enhanced adaptability, scalability, and maintainability for EIR applications.

    Conclusions:

    • PerAE offers a highly efficient and accurate solution for ECG-based identity recognition.
    • Personalized autoencoder models significantly improve the performance and practicality of biometric systems.
    • The proposed method provides a robust defense against potential attacks, enhancing user privacy in AR/VR environments.