A Computational Vision on Human Emotion
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The Symposium provides undergraduates from all academic disciplines with an opportunity to share their research with the university community and other guests through conference-style oral presentations. The Symposium represents part of a larger effort of Iowa State University to enhance, support, and celebrate undergraduate research activity.
Though coordinated by the University Honors Program, all undergraduate students are eligible and encouraged to participate in the Symposium. Undergraduates conducting research but not yet ready to present their work are encouraged to attend the Symposium to learn about the presentation process and students not currently involved in research are encouraged to attend the Symposium to learn about the broad range of undergraduate research activities that are taking place at ISU.
The first Symposium was held in April 2007. The 39 students who presented research and their mentors collectively represented all of ISU's Colleges: Agriculture and Life Sciences, Business, Design, Engineering, Human Sciences, Liberal Arts and Sciences, Veterinary Medicine, and the Graduate College. The event has grown to regularly include more than 100 students presenting on topics that span the broad range of disciplines studied at ISU.
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Late 20th century Artificial Intelligence research treated emotion and cognition as antithetical entities. Recent neurological studies, however, suggest that the two are closely related. Emotion plays a critical role in decision making. Studies also have established that neurological deficits in emotion processing lead to deficiency in decision making. These findings have invoked a new interest in the modeling of emotion in artificially intelligent systems. The Dependable Computing and Networking Lab (DCNL) at ISU, led by Dr. Arun Somani, is researching human emotion modeling using Computer Vision. The study will engender novel ideas to adapt the existing emotion-modeling framework in the research realm to the needs of the Human and Object Detection project in the DCNL group. We believe that this study could also lead to new and innovative models of human emotion. Computational tools such as OpenCV and MATLAB will be used to test and validate new models and adaptations. Using machine learning methods, the reliability and efficacy of the new methods will also be evaluated.