基于人体呼出气流体力学的用户认证系统
User authentication system based on human exhaled breath physics
AI总结：
本研究开创性地利用呼出气流的独特湍流特征构建生物识别系统，通过经典假设检验和机器学习算法实现用户确认与识别，在94人数据集上确认率超97%，并揭示了个体气道形态差异的潜在应用价值。
摘要：
这项工作以开创性的方法，尝试构建一个完全基于控制呼出气流的流体力学原理的生物识别系统。我们检验了这样一个假设：人体呼出气流中的湍流结构可以被利用来构建生物识别算法。这项工作依赖于这样一个理念：每个人的胸外气道是独一无二的，使得呼出气体成为一种生物标志物。在构建用户认证算法（即用户确认和用户识别）时，采用了包括经典多维假设检验方法和机器学习模型在内的多种方法。用户确认算法试图验证用户是否是其声称的那个人。用户识别算法则试图在没有先验信息的情况下识别用户的身份。我们使用了一个包含94名人类受试者呼出气流时间序列样本的数据集来评估这些算法的性能。对于给定的数据集，用户确认算法表现极为出色，真实确认率超过97%。基于机器学习的算法取得了良好的真实确认率，这再次印证了我们对为什么基于机器学习的算法通常优于基于经典假设检验的算法的理解。用户识别算法在提供的数据集上表现合理，超过50%的用户被识别为在两个可能的嫌疑人范围内。我们展示了呼出气流中令人惊讶的、此前从未发现的独特湍流特征。除了讨论这一新颖的生物识别系统外，我们还提出论据，主张利用这一想法作为工具，以深入了解个体间胸外气道的形态计量变异。此类工具有望在未来个性化医疗领域发挥潜力。
英文摘要：
This work, in a pioneering approach, attempts to build a biometric system that works purely based on the fluid mechanics governing exhaled breath. We test the hypothesis that the structure of turbulence in exhaled human breath can be exploited to build biometric algorithms. This work relies on the idea that the extrathoracic airway is unique for every individual, making the exhaled breath a biomarker. Methods including classical multi-dimensional hypothesis testing approach and machine learning models are employed in building user authentication algorithms, namely user confirmation and user identification. A user confirmation algorithm tries to verify whether a user is the person they claim to be. A user identification algorithm tries to identify a user's identity with no prior information available. A dataset of exhaled breath time series samples from 94 human subjects was used to evaluate the performance of these algorithms. The user confirmation algorithms performed exceedingly well for the given dataset with over $97\%$ true confirmation rate. The machine learning based algorithm achieved a good true confirmation rate, reiterating our understanding of why machine learning based algorithms typically outperform classical hypothesis test based algorithms. The user identification algorithm performs reasonably well with the provided dataset with over $50\%$ of the users identified as being within two possible suspects. We show surprisingly unique turbulent signatures in the exhaled breath that have not been discovered before. In addition to discussions on a novel biometric system, we make arguments to utilise this idea as a tool to gain insights into the morphometric variation of extrathoracic airway across individuals. Such tools are expected to have future potential in the area of personalised medicines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
