Deakin University
Browse

Echo-ID: Smart user identification leveraging inaudible sound signals

Download (2.56 MB)
Version 2 2024-06-06, 00:33
Version 1 2020-10-21, 12:06
journal contribution
posted on 2020-10-19, 00:00 authored by Syed Wajid Ali ShahSyed Wajid Ali Shah, Arash Shaghaghi, Salil S Kanhere, Jin Zhang, Adnan AnwarAdnan Anwar, Robin Ram Mohan DossRobin Ram Mohan Doss
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.

History

Journal

IEEE Access

Volume

8

Pagination

194508 - 194522

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Location

Piscataway, N.J.

ISSN

2169-3536

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2020, The Authors

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC