Thesis (Ph. D.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains xii, 106 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 100-106).

Thesis (Ph. D.)--University of Canterbury, 2007. / Typescript (photocopy). "October 2007." Includes bibliographical references (leaves 159-171). Also available via the World Wide Web.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008. / Vela, Patricio, Committee Member ; Tannenbaum, Allen, Committee Member ; Yezzi, Anthony, Committee Chair ; Turk, Greg, Committee Member ; Lanterman, Aaron, Committee Member.

Thesis (Ph.D.)--University of Delaware, 2005. / Principal faculty advisor: Chandra Kambhamettu, Dept. of Computer and Information Sciences. Includes bibliographical references.

Facial expression recognition (FER) is a research area that consists of classifying the human emotions through the expressions on their face. It can be used in applications such as biometric security, intelligent human-computer interaction, robotics, and clinical medicine for autism, depression, pain and mental health problems. This dissertation investigates the advanced technologies for facial expression analysis and develops the artificial intelligent systems for practical applications. The first part of this work applies geometric and texture domain feature extractors along with various machine learning techniques to improve FER. Advanced 2D and 3D facial processing techniques such as Edge Oriented Histograms (EOH) and Facial Mesh Distances (FMD) are then fused together using a framework designed to investigate their individual and combined domain performances. Following these tests, the face is then broken down into facial parts using advanced facial alignment and localising techniques. Deep learning in the form of Convolutional Neural Networks (CNNs) is also explored also FER. A novel approach is used for the deep network architecture design, to learn the facial parts jointly, showing an improvement over using the whole face. Joint Bayesian is also adapted in the form of metric learning, to work with deep feature representations of the facial parts. This provides a further improvement over using the deep network alone. Dynamic emotion content is explored as a solution to provide richer information than still images. The motion occurring across the content is initially captured using the Motion History Histogram descriptor (MHH) and is critically evaluated. Based on this observation, several improvements are proposed through extensions such as Average Spatial Pooling Multi-scale Motion History Histogram (ASMMHH). This extension adds two modifications, first is to view the content in different spatial dimensions through spatial pooling; influenced by the structure of CNNs. The other modification is to capture motion at different speeds. Combined, they have provided better performance over MHH, and other popular techniques like Local Binary Patterns - Three Orthogonal Planes (LBP-TOP). Finally, the dynamic emotion content is observed in the feature space, with sequences of images represented as sequences of extracted features. A novel technique called Facial Dynamic History Histogram (FDHH) is developed to capture patterns of variations within the sequence of features; an approach not seen before. FDHH is applied in an end to end framework for applications in Depression analysis and evaluating the induced emotions through a large set of video clips from various movies. With the combination of deep learning techniques and FDHH, state-of-the-art results are achieved for Depression analysis.