Title: Extended Sammon Projection and Wavelet Kernel Extreme Learning Machine for Gait-Based Legitimate User Identification on Smartphones,
Abstract: Smartphones have ubiquitously integrated into our home and work environments,
however, users normally rely on explicit but inefficient identification
processes in a controlled environment. Therefore, when a device is stolen, a
thief can have access to the owner's personal information and services against
the stored password/s. As a result of this potential scenario, this work
demonstrates the possibilities of legitimate user identification in a
semi-controlled environment through the built-in smartphones motion dynamics
captured by two different sensors. This is a two-fold process: sub-activity
recognition followed by user/impostor identification. Prior to the
identification; Extended Sammon Projection (ESP) method is used to reduce the
redundancy among the features. To validate the proposed system, we first
collected data from four users walking with their device freely placed in one
of their pants pockets. Through extensive experimentation, we demonstrate that
together time and frequency domain features optimized by ESP to train the
wavelet kernel based extreme learning machine classifier is an effective system
to identify the legitimate user or an impostor with \(97\%\) accuracy.