Title: Does Your Phone Know Your Touch?,
Abstract: This paper explores supervised techniques for continuous anomaly detection
from biometric touch screen data. A capacitive sensor array used to mimic a
touch screen as used to collect touch and swipe gestures from participants. The
gestures are recorded over fixed segments of time, with position and force
measured for each gesture. Support Vector Machine, Logistic Regression, and
Gaussian mixture models were tested to learn individual touch patterns. Test
results showed true negative and true positive scores of over 95% accuracy for
all gesture types, with logistic regression models far outperforming the other
methods. A more expansive and varied data collection over longer periods of
time is needed to determine pragmatic usage of these results.