  This paper explores the identification of smartphone users when certain
samples collected while the subject felt happy, upset or stressed were absent
or present. We employ data from 19 subjects using the StudentLife dataset, a
dataset collected by researchers at Dartmouth College that was originally
collected to correlate behaviors characterized by smartphone usage patterns
with changes in stress and academic performance. Although many previous works
on behavioral biometrics have implied that mood is a source of intra-person
variation which may impact biometric performance, our results contradict this
assumption. Our findings show that performance worsens when removing samples
that were generated when subjects may be happy, upset, or stressed. Thus, there
is no indication that mood negatively impacts performance. However, we do find
that changes existing in smartphone usage patterns may correlate with mood,
including changes in locking, audio, location, calling, homescreen, and e-mail
habits. Thus, we show that while mood is a source of intra-person variation, it
may be an inaccurate assumption that biometric systems (particularly, mobile
biometrics) are likely influenced by mood.
