Anhong Guo
Robert Xiao
Chris Harrison
ABSTRACT
User identification and differentiation have implications in many application domains, including security, personalization, and co-located multiuser systems. In response, dozens of approaches have been developed, from fingerprint and retinal scans, to hand gestures and RFID tags. In this work, we propose CapAuth, a technique that uses existing, low-level touchscreen data, combined with machine learning classifiers, to provide real-time authentication and even identification of users. As a proof-of-concept, we ran our software on an off-the-shelf Nexus 5 smartphone. Our user study demonstrates twenty-participant authentication accuracies of 99.6%. For twenty-user identification, our software achieved 94.0% accuracy and 98.2% on groups of four, simulating family use.
Supplemental Material
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Index Terms
- CapAuth: Identifying and Differentiating User Handprints on Commodity Capacitive Touchscreens
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