报告时间：3月31日（周五）10:00-11:30
报告地点：行政楼912
Shih-Chia Huang is a Full Professor with the Department of Electronic Engineering at Taipei University of Technology, and an International Adjunct Professor with the Faculty of Business and Information Technology, University of Ontario Institute of Technology, Canada. He has been named a senior member of the Institute of Electrical and Electronic Engineers (IEEE). He is currently the Chair of the IEEE Taipei Section Broadcast Technology Society. He is also a Review Panel Member of the Small Business Innovation Research (SBIR) program for the Department of Economic Development of Taipei City and New Taipei City, respectively.Professor Huang has published more than 50 journal and conference papers and holds more than 40 patents in the United States, Europe and China. In 2009, he received a doctorate degree in Electrical Engineering from National Taiwan University. Dr. Huang was presented with the Kwoh-Ting Li Young Researcher Award in 2011 by the Taipei Chapter of the Association for Computing Machinery, as well as the Dr. Shechtman Young Researcher Award in 2012 by National Taipei University of Technology. Professor Huang was the recipient of an Outstanding Research Award from Taipei University of Technology in 2014 and the College of Electrical Engineering and Computer Science, Taipei University of Technology in 2014-2015. In addition, he is an associate editor of the Journal of Artificial Intelligence and a guest editor of the Information Systems Frontiers and the International Journal of Web Services Research. He is also the Applications Track Chair and the Program Committee Chair of IEEE BigData Congress and IEEE BigData Taipei Satellite Session in 2015, and was the Program Committee Chair of IEEE BigData Taipei Satellite Session in 2014.His research interests include intelligent multimedia systems, image processing and video coding, video surveillance systems, cloud computing and big data analytics and mobile applications and systems.
报告摘要：
The development of fully automatic face annotation techniques in online social networks is currently very important for effective management and organization of the large numbers of personal photos shared on social network platforms. In this talk, we construct the personalized and adaptive Fused Face Recognition unit for each member, which uses the Adaboost algorithm to fuse several different types of base classifiers to produce highly reliable face annotation results. The proposed approach achieves a significantly higher level of efficacy, outperforming other state-of-the-art face annotation methods for real-life personal photos featuring pose variations.
