Biometric-Enabled Decision Support Platform with Risk Assessment
| dc.contributor.advisor | Yanushkevich, Svetlana | |
| dc.contributor.author | Lai, Kenneth | |
| dc.contributor.committeemember | Hatzinakos, Dimitrios | |
| dc.contributor.committeemember | Hemmati, Hadi | |
| dc.contributor.committeemember | Fear, Elise | |
| dc.contributor.committeemember | Nielsen, John | |
| dc.date | 2022-02 | |
| dc.date.accessioned | 2022-01-18T16:00:28Z | |
| dc.date.available | 2022-01-18T16:00:28Z | |
| dc.date.issued | 2022-01-14 | |
| dc.description.abstract | Biometric-based human trait and behavior recognition is a critical component of the rapidly growing domain of ambient intelligence. Particular applications of interest are biometric-enabled border checkpoints, access control, as well as healthcare and biomedical data analysis. In this thesis, we offer both theoretical and practical contributions. The main theoretical contributions include the framework for uncertainty measures and performance assessment measures in a decision support system. These measures include risk, trust, and bias and the methodology involves using these measures in a contemporary engine for decision support systems, based on causal models of uncertainty. These models allow for the prediction of events of interest and assess the risks associated with these events. Our main practical contributions include the advanced practical implementation of various machine learning approaches, mostly deep neural networks, to biometric-enabled applications such as facial recognition, action recognition, emotion classification, wearable data analysis for healthcare, and human-machine interaction applications. Demonstration of practical applications of machine reasoning for biometric-enabled systems that use facial recognition, action recognition, and emotion recognition is shown. In this research, we propose to combine multi-spectral biometric data processing, powerful deep learning techniques, along with performance improvement techniques, in a unified approach to automate face and action recognition. When combined, a platform consisting of powerful machine-learning techniques is used as a supporting tool to provide decision support for an operator. Multi-spectral data is understood as color and depth data such as video, depth, and derived skeleton joints. The deep learning techniques include convolutional and recurrent neural networks. The former is applied in our study to extract important spatial information from color and depth images, whereas the latter is utilized to recognize temporal patterns. Emerging deep learning model architectures are explored, one such network called the Residual Temporal Convolutional network offers improved performance in comparison to recurrent neural networks. This research will show the capability of using different types of data to train neural networks independently to recognize biometric patterns including actions and faces. Therefore, the main focus of this thesis is the development of a decision support platform for solving a variety of practical problems. These solutions are approached using the same methodology: advanced machine learning techniques are used to process, analyze and classify data, and machine reasoning is used as a common platform for the system-level decision making, with risk, bias, and/or trust assessment of the provided decision. This approach is embodied, in particular, in a proposed decision support system for human stress detection using physiological signals. Deep learning techniques were applied for detecting and recognizing different emotional states. The causal models were built upon the distribution of the detection and recognition scores in order to perform machine reasoning and information fusion. These results provided the operator with the risk assessment of any given scenario. Other examples of such systems are provided in multiple publications as reported in the thesis. | en_US |
| dc.identifier.citation | Lai, K. (2022). Biometric-Enabled Decision Support Platform with Risk Assessment (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.) | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/39517 | |
| dc.identifier.uri | http://hdl.handle.net/1880/114302 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject | Biometrics | en_US |
| dc.subject | Pattern Recognition | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Machine Reasoning | en_US |
| dc.subject.classification | Artificial Intelligence | en_US |
| dc.subject.classification | Engineering--Electronics and Electrical | en_US |
| dc.title | Biometric-Enabled Decision Support Platform with Risk Assessment | en_US |
| dc.type | doctoral thesis | en_US |
| thesis.degree.discipline | Engineering – Electrical & Computer | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Doctor of Philosophy (PhD) | en_US |
| ucalgary.item.requestcopy | true | en_US |