Collective human biological signal-based identification and authentication in access control environments

• Main Author: Van der Haar, Dustin Terence
• Published: 2014
• Subjects: Computers - Access control; Biometric identification; Biosensors
• Online Access: http://hdl.handle.net/10210/12392

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.