I-vector analysis for Gait-based Person Identification using smartphone inertial signals.

This paper describes and evaluates an i-vector based approach for Gait-based Person Identification (GPI) using inertial signals from a smartphone. This approach includes two variability compensation strategies (Linear Discrimination Analysis (LDA) and Probabilistic LDA) for dealing with the gait variability due to different recording sessions or different activities carried out by the user. This study uses a public available dataset that includes recordings from 30 users performing three different activities: walking, walking-upstairs and walking-downstairs.