Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate (equal error rate) of 0.7 percent an improvement ratio of 371 percent compared to previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of client's footstep data and to provide insights of the feature learning process.

中文翻译：

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使用深度残差神经网络进行鲁棒足迹识别的时空表示分析


人类足迹可以为强大的生物识别系统提供独特的行为模式。我们提出了先进计算模型中仅地板传感器数据的时空足迹表示，以进行自动生物识别验证。我们的模型提供了一种人工智能，能够有效地区分生物识别系统的合法用户（客户）和冒名顶替用户之间脚步的细粒度变化。该方法在迄今为止最大的足迹数据库中得到验证，该数据库包含来自 120 多名用户的近 20,000 个足迹信号。该数据库是通过考虑大量冒名顶替者和一小部分客户来组织的，以验证生物识别系统的可靠性。我们根据可用于模型训练的足迹数据量，提供 3 个关键数据驱动的安全场景中的实验结果：机场安全检查站（最小训练集）、工作空间环境（中等训练集）和家庭环境（最大训练集） ）。我们报告了最先进的脚步识别率，最佳相等错误接受率和错误拒绝率（相等错误率）为 0.7%，与之前最先进的技术相比，改进率为 371%。我们对深度残差神经网络进行特征分析，显示客户足迹数据的有效聚类，并提供特征学习过程的见解。