"This work, in a pioneering approach, attempts to build a biometric system that works purely with the fluid mechanics data from exhaled breath. We test the hypothesis that the structure of turbulence in exhaled human breath can be exploited to build individual 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 and machine learning models, are employed in building user authentication algorithms, namely user confirmation and user identification. 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 turbulence signatures in the exhaled breath that have not been discovered before. In addition to discussions on a novel biometric system, we make arguments to utilize this idea as a tool to gain insights into the morphometric variation of extrathoracic airway across individuals. In order to understand the physics underlying such a biometric system, we have devised a jet flow experiment with different orifice geometries. The confirmation and identification algorithms were tested for a dataset of 25 orifice geometries. Both algorithms perform with 100% accuracy on this dataset, suggesting that upstream geometry will leave an indelible fluid mechanic signature on the flow field."