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% F1 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.

中文翻译：

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使用主导频率进行个人识别的新颖足迹特征


本文有两个主要贡献。一是首次提出了主频率的提取方法。在频域中对不同受试者的脚步事件（FE）进行比较分析。此外，经过多次实验，成功完成了包含丰富特征信息的主频率的提取。另一个是新颖的脚步特征。根据主导频率开发和评估七个简单但有效的特征。 SVM 用作分类器，准确识别 FE 属于哪个人。在个人识别实验中，平均准确率为92.41%，召回率为91.3%，F1为91.85%。此外，即使在不同的信噪比下，我们的七个特征在我们的特征和之前研究的一些特征的比较中也显示出最佳的性能。值得一提的是，我们的原始信号是在噪声环境下采集的，接近真实的应用场景，没有经过任何滤波、放大等打磨。在信号较差的情况下也能获得良好的分类结果，这足以证明我们的特征是稳健且可取的。我们的人体识别方案仅涉及麦克风采集脚步声和简单的分类方法，具有计算量小、实验成本低的特点。