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This paper explores the identification of smartphone users when certainsamples collected while the subject felt happy, upset or stressed were absentor present. We employ data from 19 subjects using the StudentLife dataset, adataset collected by researchers at Dartmouth College that was originallycollected to correlate behaviors characterized by smartphone usage patternswith changes in stress and academic performance. Although many previous workson behavioral biometrics have implied that mood is a source of intra-personvariation which may impact biometric performance, our results contradict thisassumption. Our findings show that performance worsens when removing samplesthat were generated when subjects may be happy, upset, or stressed. Thus, thereis no indication that mood negatively impacts performance. However, we do findthat changes existing in smartphone usage patterns may correlate with mood,including changes in locking, audio, location, calling, homescreen, and e-mailhabits. Thus, we show that while mood is a source of intra-person variation, itmay be an inaccurate assumption that biometric systems (particularly, mobilebiometrics) are likely influenced by mood.

Studying the Impact of Mood on Identifying Smartphone Users