Biblio

With the advent of technology and owing to mankind’s reliance on technology, it is of utmost importance to safeguard people’s data and their identity. Biometrics have for long played an important role in providing that layer of security ranging from small scale uses such as house locks to enterprises using them for confidentiality purposes. In this paper we will provide an insight into behavioral biometrics that rely on identifying and measuring human characteristics or behavior. We review different types of behavioral parameters such as keystroke dynamics, gait, footstep pressure signals and more.

With the advent of the Internet of Things (IoT) and big data, high fidelity localization and tracking systems that employ cameras, RFIDs, and attached sensors intrude on personal privacy. However, the benefit of localization information sharing enables trend forecasting and automation. To address this challenge, we introduce Wobly, an attribute based signature (ABS) that measures gait. Wobly passively receives Wi-Fi beacons and produces human signatures based on the Doppler Effect and multipath signals without attached devices and out of direct line-of-sight. Because signatures are specific to antenna placement and room configuration and do not require sensor attachments, the identities of the individuals can remain anonymous. However, the gait based signatures are still unique, and thus Wobly is able to track individuals in a building or home. Wobly uses the physical layer channel and the unique human gait as a means of encoding a person's identity. We implemented Wobly on a National Instruments Radio Frequency (RF) test bed. Using a simple naive Bayes classifier, the correct identification rate was 87% with line-of-sight (LoS) and 77% with non-line-of-sight (NLoS).