Uncovering perceived identification accuracy of in-vehicle biometric sensing
Biometric techniques can help make vehicles safer to drive, authenticate users, and provide personalized in-car experiences. However, it is unclear to what extent users are willing to trade their personal biometric data for such benefits. In this early work, we conducted an open card sorting study (N=11) to better understand how well users perceive their physical, behavioral and physiological features can personally identify them. Findings showed that on average participants clustered features into six groups, and helped us revise ambiguous cards and better understand users’ clustering. These findings provide the basis for a follow up online closed card sorting study to more fully understand perceived identification accuracy of (in-vehicle) biometric sensing. By uncovering this at a larger scale, we can then further study the privacy and user experience trade-off in (automated) vehicles.
|Keywords||Biometrics, Card sorting, In-vehicle, Perceived accuracy, Privacy, Sensing|
|Conference||ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications|
El Ali, A, Ashby, L.M, Webb, A.M, Zwitser, R, & César Garcia, P.S. (2019). Uncovering perceived identification accuracy of in-vehicle biometric sensing. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications: Adjunct Proceedings (pp. 327–334). doi:10.1145/3349263.3351506