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Shallow Neural Network for Biometrics from the ECG-WATCH

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Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12463))

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Abstract

Applications such as surveillance, banking and healthcare deal with sensitive data whose confidentiality and integrity depends on accurate human recognition. In this sense, the crucial mechanism for performing an effective access control is authentication, which unequivocally yields user identity. In 2018, just in North America, around 445K identity thefts have been denounced. The most adopted strategy for automatic identity recognition uses a secret for encrypting and decrypting the authentication information. This approach works very well until the secret is kept safe. Electrocardiograms (ECGs) can be exploited for biometric purposes because both the physiological and geometrical differences in each human heart correspond to uniqueness in the ECG morphology. Compared with classical biometric techniques, e.g. fingerprints, ECG-based methods can definitely be considered a more reliable and safer way for user authentication due to ECG inherent robustness to circumvention, obfuscation and replay attacks. In this paper, the ECG WATCH, a non-expensive wristwatch for recording ECGs anytime, anywhere, in just 10 s, is proposed for user authentication. The ECG WATCH acquisitions have been used to train a shallow neural network, which has reached a 99% classification accuracy and 100% intruder recognition rate.

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Author information

Authors and Affiliations

  1. DET, Politecnico di Torino, Turin, Italy

    Vincenzo Randazzo & Eros Pasero

  2. University of Picardie Jules Verne, Amiens, France

    Giansalvo Cirrincione

  3. University of South Pacific, Suva, Fiji

    Giansalvo Cirrincione

Authors
  1. Vincenzo Randazzo
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  2. Giansalvo Cirrincione
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  3. Eros Pasero
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Corresponding author

Correspondence to Vincenzo Randazzo .

Editor information

Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Randazzo, V., Cirrincione, G., Pasero, E. (2020). Shallow Neural Network for Biometrics from the ECG-WATCH. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_22

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  • DOI: https://doi.org/10.1007/978-3-030-60799-9_22

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