Write to know: on the feasibility of wrist motion based user-authentication from handwriting
Proceedings of the 14th ACM Conference on Security and Privacy in Wireless …, 2021•dl.acm.org
The popularity of smart wrist wearable technology (eg, smart-watches) has rejuvenated the
exploration of dynamic biometric-based authentication techniques that employ sensor data
from these devices. Despite the progress demonstrated by the scientific community,
research in this area has not successfully transitioned to practice, and we are yet to see a
mainstream user-authentication product based on a dynamic biometric such as
handwriting/hand gestures captured using commercial wrist wearables. This work …
exploration of dynamic biometric-based authentication techniques that employ sensor data
from these devices. Despite the progress demonstrated by the scientific community,
research in this area has not successfully transitioned to practice, and we are yet to see a
mainstream user-authentication product based on a dynamic biometric such as
handwriting/hand gestures captured using commercial wrist wearables. This work …
The popularity of smart wrist wearable technology (e.g., smart-watches) has rejuvenated the exploration of dynamic biometric-based authentication techniques that employ sensor data from these devices. Despite the progress demonstrated by the scientific community, research in this area has not successfully transitioned to practice, and we are yet to see a mainstream user-authentication product based on a dynamic biometric such as handwriting/hand gestures captured using commercial wrist wearables. This work undertakes an investigative analysis to further explore why that is the case. We accomplish this by studying the feasibility and practical deployability of handwriting-based authentication techniques in the literature that utilize motion sensors on-board wrist wearables. We conduct this analysis by replicating four state-of-the-art and representative handwriting-based authentication schemes that employ wrist motion data, in order to test their viability in realistic hand-writing/gesture scenarios. By using data collected from actual human subjects in an unconstrained fashion, we comparatively evaluate the performance of these schemes with well-defined usability and security metrics. Our experimental results show that some of the tested schemes perform considerably well in practice, and are promising. However, they do suffer from several practical user-dependent and technique-specific challenges that act as roadblocks towards their wide-scale adoption in mainstream applications.
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