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Title
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Attribute prediction from near infrared iris and ocular images
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Creator
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Bobeldyk, Denton
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Date
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2019
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Collection
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Electronic Theses & Dissertations
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Description
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The iris is the colored portion of the eye surrounding the pupil. Images captured in the visible spectrum make it difficult for the rich texture of brown irides to be discerned; therefore, iris recognition systems typically capture an image in the Near Infrared (NIR) spectrum. The region surrounding the iris, the ocular region, is also captured by the sensor during the imaging process.The focus of this thesis is on developing methods for predicting soft biometric attributes of an individual...
Show moreThe iris is the colored portion of the eye surrounding the pupil. Images captured in the visible spectrum make it difficult for the rich texture of brown irides to be discerned; therefore, iris recognition systems typically capture an image in the Near Infrared (NIR) spectrum. The region surrounding the iris, the ocular region, is also captured by the sensor during the imaging process.The focus of this thesis is on developing methods for predicting soft biometric attributes of an individual based on the iris and ocular components of the eye. In addition to attribute prediction, the effect of covariates on attribute prediction are also studied. Attributes considered in this work include gender, race and eye color. For the gender and race attributes, both the iris and surrounding ocular region are analyzed to determine which region provides the greatest gender cues. A regional analysis reveals that the iris-excluded ocular region provides a greater gender prediction accuracy than the iris-only region. This finding is of great significance as, typically, the iris-excluded ocular region is discarded by the iris recognition system. This research reinforces the need to retain the iris-excluded ocular region for additional processing. For race, it is shown that the iris-only region provides better prediction accuracy. In order to study the stability of the gender and race features, the impact of image blur on attribute prediction was also examined. It is observed that as the level of image blur increases, the race prediction accuracy decays at a much faster rate than that of gender. For eye color, the textual cues presented on the iris stroma are exploited to generate a discriminatory feature vector that is capable of distinguishing between two categories of eye color. The impact of image resolution on attribute prediction was also determined. A convolutional neural network architecture is presented that is capable of attribute prediction using images as small as 5x6, a mere 30 pixels. Experimental results suggest the possibility of deducing soft biometric attributes from low resolution images, thereby underscoring the feasibility of extracting these attributes from poor quality images. Finally, the thesis explores the possibility of harnessing the feature vector used to predict one attribute (e.g., gender) in order to predict a different attribute (e.g. race). The ensuing experiments convey the viability of cross attribute prediction in the context of NIR ocular images.In summary, this thesis provides insight into attribute prediction from NIR ocular images by conducting an extensive set of experiments.
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Title
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Teaching and learning with digital evolution : factors influencing implementation and student outcomes
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Creator
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Lark, Amy M.
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Date
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2014
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Collection
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Electronic Theses & Dissertations
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Description
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Science literacy for all Americans has been the rallying cry of science education in the United States for decades. Regardless, Americans continue to fall short when it comes to keeping pace with other developed nations on international science education assessments. To combat this problem, recent national reforms have reinvigorated the discussion of what and how we should teach science, advocating for the integration of disciplinary core ideas, crosscutting concepts, and science practices....
Show moreScience literacy for all Americans has been the rallying cry of science education in the United States for decades. Regardless, Americans continue to fall short when it comes to keeping pace with other developed nations on international science education assessments. To combat this problem, recent national reforms have reinvigorated the discussion of what and how we should teach science, advocating for the integration of disciplinary core ideas, crosscutting concepts, and science practices. In the biological sciences, teaching the core idea of evolution in ways consistent with reforms is fraught with challenges. Not only is it difficult to observe biological evolution in action, it is nearly impossible to engage students in authentic science practices in the context of evolution. One way to overcome these challenges is through the use of evolving populations of digital organisms.Avida-ED is digital evolution software for education that allows for the integration of science practice and content related to evolution. The purpose of this study was to investigate the effects of Avida-ED on teaching and learning evolution and the nature of science. To accomplish this I conducted a nationwide, multiple-case study, documenting how instructors at various institutions were using Avida-ED in their classrooms, factors influencing implementation decisions, and effects on student outcomes. I found that all of the participating instructors held views on teaching and learning that were well aligned with reform-based pedagogy, and although instructors used Avida-ED in a variety of ways, all adopted learner-centered pedagogical strategies that focused on the use of inquiry. After implementation, all of the instructors indicated that Avida-ED had allowed them to teach evolution and the nature of science in ways consistent with their personal teaching philosophies. In terms of assessment outcomes, students in lower-division courses significantly improved both their understanding and acceptance of evolution after using Avida-ED, and learning of content was positively associated with increased acceptance. Although student learning outcomes and instructor familiarity with Avida-ED were not associated with student affective response to the program, instructor familiarity was highly influential with regard to both how Avida-ED was implemented and student affective response, particularly student interest, enjoyment, and self-efficacy. The results of this dissertation provide strong evidence suggesting that Avida-ED is a promising tool for teaching and learning about evolution in reform-based ways, and suggest that improving instructor pedagogical content knowledge with regard to research-based tools like Avida-ED may be implicated in generating student interest in STEM.
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