Abstract
Ph.D. (Computer Science)
The introduction of new portable sensors that monitor physiological systems
in the human body has allowed quality of life and medical diagnostic applications
to be taken directly to the user, without the constraints of physical
space or inconvenience. The potential of these sensors in the domain of authentication
and identi cation is becoming more feasible each day and current
research in these biometric systems show a great deal of promise. Novel biometric
systems are being introduced that use biological signals (also known
as biosignals) in the human body captured by these sensors (such as brain
waves or heart rate) as the core unique attribute.
The study builds on the proliferation of these sensors and proposes an interoperable
model called CoBI, which allows individual or multi-factor authentication
and identi cation to take place. The model provides a platform
for any viable biosignal that can be used for the purposes of identi cation
and authentication, by providing pluggable sensor and signal processing components.
These components can then convert biosignals into a common format,
a feature vector consisting of estimated autoregressive (AR) coe cients.
Once they are in a common format they can then be merged together to form
a consolidated feature vector using feature fusion. This consolidated feature
vector can then be persisted during enrolment or passed further for matching
using classi cation techniques, such as K-Nearest Neighbour.
The results, from the comprehensive benchmark performed (called BAMBI)
on an implemented version of the model (called CaNViS), have shown that
biological signals that contain cardiac and neurological components (ie. from
an electrocardiogram (ECG) and electroencephalogram (EEG), respectively)
can be captured, processed, consolidated and classi ed using the CoBI model
successfully. By utilising the correct AR model order during feature estimation
for the cardiac and neurological components, along with the appropriate
classi er for matching, the biometric system yields nominal results for authentication
and identi cation in access control environments.