{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/44406"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/44406","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Image-based facial expression recognition","abstract":"This work deals with facial expression recognition from various face representations. The key contributions of the work are both empirical and theoretical. In empirical work, we first design and successfully test a framework for near-frontal expression recognition. Then we do a significance analysis of the effect of changes in pan and tilt angles on expression recognition performance for non-frontal facial expression recognition. This analysis is followed by `view-fusion' across multiple views in various configurations to come up with joint decisions. These results have significant practical implications. The theoretical contributions of the work are the development of Supervised Soft Vector Quantization and Supervised Hierarchical Gaussianization algorithms. The key idea in both of these is to iteratively update the Gaussian Mixture Models in such a way that they are more suitable for classification in these respective frameworks. We then present some exciting results for both the algorithms. These results show a considerable increase in performance compared to the unsupervised counterparts. The algorithmic improvements are significant since they enhance the established image classification frameworks. The supervised algorithms are general and can be applied to any image categorization task.","abstract_html":"This work deals with facial expression recognition from various face representations. The key contributions of the work are both empirical and theoretical. In empirical work, we first design and successfully test a framework for near-frontal expression recognition. Then we do a significance analysis of the effect of changes in pan and tilt angles on expression recognition performance for non-frontal facial expression recognition. This analysis is followed by `view-fusion' across multiple views in various configurations to come up with joint decisions. These results have significant practical implications. The theoretical contributions of the work are the development of Supervised Soft Vector Quantization and Supervised Hierarchical Gaussianization algorithms. The key idea in both of these is to iteratively update the Gaussian Mixture Models in such a way that they are more suitable for classification in these respective frameworks. We then present some exciting results for both the algorithms. These results show a considerable increase in performance compared to the unsupervised counterparts. The algorithmic improvements are significant since they enhance the established image classification frameworks. 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