دانلود مقاله انگلیسی رایگان:Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG - 2021
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  • Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG
    Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG

    سال انتشار:

    2021


    عنوان انگلیسی مقاله:

    Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG


    ترجمه فارسی عنوان مقاله:

    Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG


    منبع:

    Sciencedirect - Elsevier - Biomedical Signal Processing and Control, 68 (2021) 102689: doi:10:1016/j:bspc:2021:102689


    نویسنده:

    Yefei Zhang


    چکیده انگلیسی:

    Increasingly smart techniques for counterfeiting face and fingerprint traits have increased the potential threats to information security systems, creating a substantial demand for improved security and better privacy and identity protection. The internet of Things (IoT)-driven fingertip electrocardiogram (ECG) acquisition provides broad application prospects for ECG-based identity systems. This study focused on three major impediments to fingertip ECG: the impact of variations in acquisition status, the high computational complexity of traditional convolutional neural network (CNN) models and the feasibility of model migration, and a lack of sufficient fingertip samples. Our main contribution is a novel fingertip ECG identification system that integrates transfer learning and a deep CNN. The proposed system does not require manual feature extraction or suffer from complex model calculations, which improves its speed, and it is effective even when only a small set of training data exists. Using 1200 ECG recordings from 600 individuals, we consider 5 simulated yet potentially practical scenarios. When analyzing the overall training accuracy of the model, its mean accuracy for the 540 chest- collected ECG from PhysioNet exceeded 97.60 %, and for 60 subjects from the CYBHi fingertip-collected ECG, its mean accuracy reached 98.77 %. When simulating a real-world human recognition system on 5 public datasets, the validation accuracy of the proposed model can nearly reach 100 % recognition, outperforming the original GoogLeNet network by a maximum of 3.33 %. To some degree, the developed architecture provides a reference for practical applications of fingertip-collected ECG-based biometric systems and for information network security.
    Keywords: Off-the-person | Fingertip ECG biometric | Human identification | Convolutional neural network (CNN) | Transfer learning


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 4209 کیلوبایت

    قیمت: رایگان


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