Poster: Fingerprint-Face Friction Based Earable Authentication

Zi Wang, Yilin Wang, Yingying Chen, Jie Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Ear wearables (earables) have become an emerging and wide acceptable platform for various applications. Because of the limited input interface of earables, traditional authentication methods become less desired. However, the feature-rich sensing abilities of earables and the unique human face-ear channel bring us new sensing opportunities to reutilize fingerprints. In this work, we proposed SlidePass, a secure earables authentication system that leverages the finger-face acoustic friction produced by sliding finger gestures on the face. In particular, our system leverages the inward-facing microphone of the earables to reliably capture the acoustic of finger-face frictions. The core insight of our system is to utilize the face as a natural scanner for finger-face friction and earables to capture and reconstruct the fingerprint features. SlidePass is specially designed for earables. Due to the finger-face friction captured and encrypted by the face channel that is unique and hidden in the human skull, SlidePass is more resistant to various spoofing attacks. Our preliminary evaluation included ten different fingerprints showing that SlidePass achieves an average accuracy of 94%.

Original languageEnglish (US)
Title of host publicationCCS 2022 - Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Number of pages3
ISBN (Electronic)9781450394505
StatePublished - Nov 7 2022
Externally publishedYes
Event28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022 - Los Angeles, United States
Duration: Nov 7 2022Nov 11 2022

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221


Conference28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022
Country/TerritoryUnited States
CityLos Angeles

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications


  • biometrics
  • earable
  • fingerprint
  • friction
  • user authentication


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