6 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
Deep Temporal Convolution Network for Time Series Classification
A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data. In this work, the temporal context of the time series data is chosen as the useful aspect of the data that is passed through the network for learning. By exploiting the compositional locality of the time series data at each level of the network, shift-invariant features can be extracted layer by layer at different time scales. The temporal context is made available to the deeper layers of the network by a set of data processing operations based on the concatenation operation. A matching learning algorithm for the revised network is described in this paper. It uses gradient routing in the backpropagation path. The framework as proposed in this work attains better generalization without overfitting the network to the data, as the weights can be pretrained appropriately. It can be used end-to-end with multivariate time series data in their raw form, without the need for manual feature crafting or data transformation. Data experiments with electroencephalogram signals and human activity signals show that with the right amount of concatenation in the deeper layers of the proposed network, it can improve the performance in signal classification
Heartrate-Dependent Heartwave Biometric Identification With Thresholding-Based GMM–HMM Methodology
This paper presents an adaptive heartrate-dependent heartwave-signal-based biometric identification. A reliable and continuous heartwave extraction method featuring the hybridized discrete waveform transform method with heartrate adaptive QT and PR intervals to perform comprehensive heartwave features extractions on more than 35 000 heartwave signal. The size of training data was determined and the hybridized Gaussian-mixture-model-hidden-Markov-model classification method was used in the classification. Dynamic thresholding criterial incorporating user-specific scores and heartrate were adopted. The identification process using dynamic thresholding criterial achieved a remarkable receiver operating characteristic of 0.89 in true positive rate and an equal error rate of 0.11