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.
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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.