Multi-Modal User Authentication Using Biometrics
The connectivity of smart technology is ever increasing with the expansion of internet availability. Emerging applications such as financial transactions, healthcare check-ups, and property access can be made through smart technologies. This also presents a new vulnerability as hackers have more opportunities to attack users. Therefore, there is an immediate emphasis on a strong authentication system. While passwords, PINs, or pattern locks can overwhelm users, active biometric schemes like retina scans require active use and cannot be used in continuous situations. A solution to this is the use of implicit continuous biometrics such as heart rate, gait, and breathing patterns. In this work, we present two context-dependent soft-biometric-based wearable authentication system strategies utilizing the heart rate, gait, and breathing audio signals. From our detailed analysis, we find that in a sedentary state, a binary support vector machine with radial basis function (RBF) kernel can achieve an average accuracy of $0.94 \pm 0.07$ and $F_1$ score of $0.93 \pm 0.08$. In a non-sedentary state, $k$- Nearest Neighbors ($k$=2) can achieve an average accuracy of $0.93 \pm 0.06$ and $F_1$ score of $0.93 \pm 0.03$, which shows the promise of this work. Considering the availability of a single user’s data, we develop unary models and obtain an average accuracy of $0.72 \pm 0.10$ and a $F_1$ score $0.73 \pm 0.06$ when sedentary and $0.72 \pm 0.10$ and a $F_1$ score $0.72 \pm 0.09$ respective scores when non-stationary.
Computer science|Information science|Bioinformatics
Cheung, William, "Multi-Modal User Authentication Using Biometrics" (2021). ETD Collection for Fordham University. AAI28315954.