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Wearable devices are increasingly used, thanks to the wide set ofapplications that can be deployed exploiting their ability to monitor physicalactivity and health-related parameters. Their usage has been recently proposedto perform biometric recognition, leveraging on the uniqueness of the recordedtraits to generate discriminative identifiers. Most of the studies conducted onthis topic have considered signals derived from cardiac activity, detecting itmainly using electrical measurements thorugh electrocardiography, or opticalrecordings employing photoplethysmography. In this paper we instead propose aBIOmetric recognition approach using Wearable Inertial Sensors detecting Heartactivity (BIOWISH). In more detail, we investigate the feasibility ofexploiting mechanical measurements obtained through seismocardiography andgyrocardiography to recognize a person. Several feature extractors andclassifiers, including deep learning techniques relying on transfer learningand siamese training, are employed to derive distinctive characteristics fromthe considered signals, and differentiate between legitimate and impostorsubjects. An multi-session database, comprising acquisitions taken fromsubjects performing different activities, is employed to perform experimentaltests simulating a verification system. The obtained results testify thatidentifiers derived from measurements of chest vibrations, collected bywearable inertial sensors, could be employed to guarantee high recognitionperformance, even when considering short-time recordings.

BIOWISH: Biometric Recognition using Wearable Inertial Sensors detecting Heart Activity