1 research outputs found
Heartwave biometric authentication using machine learning algorithms
PhD ThesisThe advancement of IoT, cloud services and technologies have prompted heighten
IT access security. Many products and solutions have implemented biometric solution
to address the security concern. Heartwave as biometric mode offers the potential due
to the inability to falsify the signal and ease of signal acquisition from fingers. However
the highly variated heartrate signal, due to heartrate has imposed much headwinds in
the development of heartwave based biometric authentications.
The thesis first review the state-of-the-arts in the domains of heartwave
segmentation and feature extraction, and identifying discriminating features and
classifications. In particular this thesis proposed a methodology of Discrete Wavelet
Transformation integrated with heartrate dependent parameters to extract
discriminating features reliably and accurately.
In addition, statistical methodology using Gaussian Mixture Model-Hidden
Markov Model integrated with user specific threshold and heartrate have been proposed
and developed to provide classification of individual under varying heartrates. This
investigation has led to the understanding that individual discriminating feature is a
variable against heartrate.
Similarly, the neural network based methodology leverages on ensemble-Deep
Belief Network (DBN) with stacked DBN coded using Multiview Spectral Embedding
has been explored and achieved good performance in classification. Importantly, the
amount of data required for training is significantly reduce