User authentication system based on human exhaled breath physics
Mukesh Karunanethy Rahul Tripathi Mahesh V Panchagnula Raghunathan Rengaswamy
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.