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Transparent Authentication: Utilising Heart Rate for User Authentication
Enamamu T, Clarke NL, Haskell-Dowland PS (Dowland PS), Li F
International Conference for Internet Technology and Secured Transactions (ICITST-2017) , pp283-289, ISBN:978-1-9083-207-97, 2017
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There has been exponential growth in the use of wearable technologies in the last decade with smart watches having a large share of the market. Smart watches were primarily used for health and fitness purposes but recent years have seen a rise in their deployment in other areas. Recent smart watches are fitted with sensors with enhanced functionality and capabilities. For example, some function as standalone device with the ability to create activity logs and transmit data to a secondary device. The capability has contributed to their increased usage in recent years with researchers focusing on their potential. This paper explores the ability to extract physiological data from smart watch technology to achieve user authentication. The approach is suitable not only because of the capacity for data capture but also easy connectivity with other devices – principally the Smartphone. For the purpose of this study, heart rate data is captured and extracted from 30 subjects continually over an hour. While security is the ultimate goal, usability should also be key consideration. Most bioelectrical signals like heart rate are non-stationary time-dependent signals therefore Discrete Wavelet Transform (DWT) is employed. DWT decomposes the bioelectrical signal into n level sub-bands of detail coefficients and approximation coefficients. Biorthogonal Wavelet (bior 4.4) is applied to extract features from the four levels of detail coefficients. Ten statistical features are extracted from each level of the coefficient sub-band. Classification of each sub-band levels are done using a Feedforward neural Network (FF-NN). The 1st, 2nd, 3rd and 4th levels had an Equal Error Rate (EER) of 17.20%, 18.17%, 20.93% and 21.83% respectively. To improve the EER, fusion of the four level sub-band is applied at the feature level. The proposed fusion showed an improved result over the initial result with an EER of 11.25%. As a one-off authentication decision, an 11% EER is not ideal, its use on a continuous basis makes this more than feasible in practice.

Enamamu T, Clarke NL, Haskell-Dowland PS (Dowland PS), Li F