Mood Versus Identity: Studying the Influence of Affective States on Mobile Biometrics

Mobile device usage data such as mobile app use and acceleration measurements fluctuate often as individuals carry out their daily tasks. As these data have emerged in recent years as promising biometric identifiers, it is important to understand the many causes of these variations such that these systems can adapt without degradation in performance. In this paper, we seek to understand the impact of changes in a person's mood on the performance of a mobile biometric system using a publicly available dataset of 27 subjects. We explore the verification and identification tasks, along with mood prediction from smartphone data. We achieved an equal error rate of 3% and a d-prime value of 5.05 for the verification task, wherein experiments showed that verification is minimally influenced by an individual's mood, although negative arousal slightly degraded performance. We created a multi-class problem to study the identification task, achieving an average 83% F 1-score. Here, we observed that subjects with lower identification accuracy (<; 70%) experienced fewer mood changes compared to the average (13.48), while all but one subject with high identification accuracy (>95%) experienced more mood changes compared to the average. Contrasting previous claims, our findings suggest that frequent changes in mood may have little negative impact performance. Finally, positive arousal and negative valence yielded the highest area under the curve (0.67) for mood prediction. This was also the class associated with the highest average genuine and lowest average imposter scores for verification experiments, suggesting a correspondence between recognition and mood prediction tasks that applications such as sensor-enhanced mHealth apps could leverage.