報告題目：Artificial Intelligence based on Visual Analytics
報告人：郭國棟副教授（Associate Professor）
單位：美國西弗吉尼亞大學計算機科學與電子工程系
報告時間：2018年5月21日（周一）下午15:00-16:00
報告地點：翡翠科教樓A座二樓A205會議室
報告人簡介：
Dr. Guodong Guo is an Associate Professor at the Department of Computer Science and Electrical Engineering, West Virginia University. He got his Ph.D. and Master degrees in Computer Science from the University of Wisconsin – Madison, and his Bachelor’s degree in Automation from Tsinghua University, P. R. China. He had visited or worked in several places, including INRIA in France, Microsoft Research Asian, Microsoft Research Redmond, and Mitsubishi Electric Research Laboratories in Boston. His research areas include Computer Vision, Biometrics, Machine Learning, and Human Computer Interaction. He has published three books and about 100 technical papers, and filed three patents. His papers have been cited more than 6,000 times. His research has been funded by the NSF, DoJ, NSF-CITeR, NIH, DoD, USDA, NC Space New Investigation award, and CHDI Foundation. Recently, his papers were selected as “The Best of Face and Gesture 2013” and “The Best of Face and Gesture 2015,” respectively. His research on BMI from face photos was reported by Scientific News extensively. He won the “2015 Looking at People ICCV Challenge” competition at the top-three, and received the Outstanding Researcher Award at West Virginia University, and North Carolina State Award for Excellence in Innovation.
報告摘要：
Artificial Intelligence (AI) has become very hot recently, in both the research community and business investment. Visual analytics is one important aspect in developing AI technologies, which has broad applications in video surveillance, business intelligence, next generation robotics, wearable computing, identity management, smart environment, smart healthcare, etc. Although very useful, it is still a great challenge to perform visual analytics accurately and robustly.In this talk, I will presentsome ofmy researchworks onvisual data analysis. Some specific topics includehuman age estimation, demographic classification, body mass index (BMI) estimation from facial appearance, and face matching with various changes.
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