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.

翻译：本工作以开创性方法尝试构建一种纯粹基于呼出气流流体力学特性的生物识别系统。我们验证了以下假设：人体呼出气流的湍流结构可被用于构建生物识别算法。该研究基于这样一个核心理念——每个人的胸外气道具有独特性，使得呼出气流成为生物标志物。我们采用经典多维假设检验方法与机器学习模型构建用户认证算法，包括用户确认与用户识别两类。用户确认算法旨在验证用户是否为其所声称的身份，用户识别算法则在无先验信息条件下识别用户身份。通过采集94名受试者的呼出气流时间序列样本数据集评估算法性能。实验结果表明，用户确认算法在给定数据集上表现极为优异，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，再次印证了为何这类算法通常优于经典假设检验算法。用户识别算法在给定数据集上表现合理，超过50%的用户能被锁定在两名可疑嫌疑人范围内。我们发现呼出气流中存在着此前未被揭示的惊人独特湍流特征。除探讨新型生物识别系统外，我们还论证了利用该理念作为工具以探究个体间胸外气道形态差异的可行性。此类工具预期将在个性化医疗领域具有重要应用潜力。