Person Identification based on Physiological Signals: Conditions and Risks

Abstract

: Person identification is usually based on video signals, DNA samples or fingerprints. In this study, we want to show the effectiveness of other physiological signals for person identification. For this purpose, we evaluate different settings with the SenseEmotion Database. The data set was initially collected for research purposes in the fields of emotion and pain intensity recognition. However, we use the multi-modality of this database to evaluate the effectiveness of different physiological signals, such as the heart activity or skin conductance, for person identification purposes. It is almost impossible for human beings to identify persons by evaluating a set of different fingerprints. Machine learning methods usually outperform humans in both, operation time as well as accuracy, in those tasks. In our study, we show that basic pattern recognition models can be used to identify human beings based on physiological signals. However, our outcomes show that person identification based on physiological signals must be treated with caution. Specifically, our results indicate that it is essential to include physiological signals from different recording sessions, to ensure generalisation ability of the classification model, for the person identification task.