Browsing by Author "Lai, Kenneth"

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    Biometric-Enabled Decision Support Platform with Risk Assessment
    (2022-01-14) Lai, Kenneth; Yanushkevich, Svetlana; Hatzinakos, Dimitrios; Hemmati, Hadi; Fear, Elise; Nielsen, John
    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.
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    Facial Biometrics using RGB-Depth Data
    (2015-05-01) Lai, Kenneth; Yanushkevich, Svetlana
    This thesis focuses on facial biometrics, acquired using multi-spectral sensors, such as RGB, depth, and infrared. This data is used for authorizing users of automated and semi-automated access systems, as well as for accumulating additional information regarding facial biometrics. In this work we show that depth data, taken using an inexpensive RGB-D sensor, helps find the head pose of a subject and extract frontal-view frames. We prove experimentally that usage of the frontal-view frames improves the efficiency of face recognition. Additionally, corresponding synchronized infrared video frames allow for more efficient temperature estimation from the frontal-views. Furthermore, this thesis describes potential applications of this approach in biometric-based security systems, as well as in biomedical and health care solutions. This also shows the promising future of using biometrics in natural and contactless control interfaces.