Novel Footstep Features Using Dominant Frequencies for Personal Recognition

There are two main contributions in this article. One is that an extraction means of dominant frequencies is proposed for the first time. The footstep events (FEs) from diverse subjects are analyzed comparatively in frequency domain. Besides the extraction of dominant frequencies containing rich feature information is successfully accomplished after numerous experiments. The other is novel footstep features. Seven simple but effective features are developed and assessed based on dominant frequencies. A SVM is utilized as classifier, exactly identifying which person a FE belongs to. 92.41% precision, 91.3% recall and 91.85% ${F}_{1}$ on average are obtained in personal recognition experiment. Moreover, our seven features show a best performance in the comparison about our features and some features studied previously, even under various SNR. It is worth mentioning that our original signals are collected in noisy environment to approach real application scenarios, without any polishing such as filtering, amplifying. Good classification results could be acquired with poor signal, which is enough to demonstrate that our features are robust and preferable. Our human recognition scheme only involves a microphone to collect footstep sounds and easy classification method, with the characteristics of small calculation and low experimental cost.