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. Attached Files Name Description MIMEType Size Downloads Title Echo-ID: Smart user identification leveraging inaudible sound signals Author(s) Shah, Syed Wajid Ali Shaghaghi, Arash Kanhere, Salil S. Zhang, Jin Anwar, Adnan orcid.org/0000-0003-3916-1381 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 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Wed, 21 Oct 2020, 11:06:54 EST Thu, 22 Oct 2020, 04:00:56 EST Thu, 22 Oct 2020, 08:30:28 EST Wed, 04 Nov 2020, 05:03:55 EST Wed, 04 Nov 2020, 13:23:09 EST Wed, 04 Nov 2020, 13:32:09 EST Wed, 04 Nov 2020, 14:56:10 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 32 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Thu, 22 Oct 2020, 08:30:28 EST

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.