Person identification by analyzing door accelerations in time and frequency domain

Abstract

The paper describes an approach for recognizing a person entering a room using only door accelerations. The approach analyzes the acceleration signal in time and frequency domain. For each domain two types of methods were developed: (i) feature-based – use features to describe the acceleration and then uses classification method to identify the person; (ii) signal-based – use the acceleration signal as input and finds the most similar ones in order to identify the person. The four methods were evaluated on a dataset of 1005 entrances recorded by 12 people. The results show that the time-domain methods achieve significantly higher accuracy compared to the frequency-domain methods, with signal-based method achieving 86% accuracy. Additionally, the four methods were combined and all 15 combinations were examined. The best performing combined method increased the accuracy to 90%. The results confirm that it is possible to identify a person entering a room using the door’s acceleration.