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Echo-ID: Smart user identification leveraging inaudible sound signals

Shah, Syed Wajid Ali, Shaghaghi, Arash, Kanhere, Salil S., Zhang, Jin, Anwar, Adnan and Ram Mohan Doss, Robin 2020, Echo-ID: Smart user identification leveraging inaudible sound signals, IEEE Access, vol. 8, pp. 194508-194522, doi: 10.1109/ACCESS.2020.3031899.

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Title Echo-ID: Smart user identification leveraging inaudible sound signals
Author(s) Shah, Syed Wajid Ali
Shaghaghi, Arash
Kanhere, Salil S.
Zhang, Jin
Anwar, AdnanORCID iD for Anwar, Adnan orcid.org/0000-0003-3916-1381
Ram Mohan Doss, RobinORCID iD for Ram Mohan Doss, Robin orcid.org/0000-0001-6143-6850
Journal name IEEE Access
Volume number 8
Start page 194508
End page 194522
Total pages 16
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Place of publication Piscataway, N.J.
Publication date 2020-10-19
ISSN 2169-3536
Keyword(s) smart-spaces
user identification
sound-signals
Summary In this article, we present a novel user identification mechanism for smart spaces called Echo-ID (referred to as E-ID). Our solution relies on inaudible sound signals for capturing the user’s behavioral tapping/typing characteristics while s/he types the PIN on a PIN-PAD, and uses them to identify the corresponding user from a set of N enrolled inhabitants. E-ID proposes an all-inclusive pipeline that generates and transmits appropriate sound signals, and extracts a user-specific imprint from the recorded signals (E-Sign). For accurate identification of the corresponding user given an E-Sign sample, E-ID makes use of deep-learning (i.e., CNN for feature extraction) and SVM classifier (for making the identification decision). We implemented a proof of the concept of E-ID by leveraging the commodity speaker and microphone. Our evaluations revealed that E-ID can identify the users with an average accuracy of 93% to 78% from an enrolled group of 2-5 subjects, respectively.
Language eng
DOI 10.1109/ACCESS.2020.3031899
Indigenous content off
Field of Research 08 Information and Computing Sciences
09 Engineering
10 Technology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2020, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30144362

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.