Ching Y. Suen
Director, Centre for Pattern Recognition and Machine Intelligence
Concordia University, Canada
Dr. Ching Y. Suen is the Director of CENPARMI and the Concordia Chair on AI & Pattern Recognition. He received his Ph.D. degree from UBC (Vancouver) and his Master's degree from the University of Hong Kong. He has served as the Chairman of the Department of Computer Science and as the Associate Dean (Research) of the Faculty of Engineering and Computer Science of Concordia University. Prof. Suen has served at numerous national and international professional societies as President, Vice-President, Governor, and Director. He has given 45 invited/keynote papers at conferences and 195 invited talks at various industries and academic institutions around the world. He has been the Principal Investigator or Consultant of 30 industrial projects. His research projects have been funded by the ENCS Faculty and the Distinguished Chair Programs at Concordia University, FCAR (Quebec), NSERC (Canada), the National Networks of Centres of Excellence (Canada), the Canadian Foundation for Innovation, and the industrial sectors in various countries, including Canada, France, Japan, Italy, and the United States. Dr. Suen has published 4 conference proceedings, 12 books and more than 495 papers, and many of them have been widely cited while the ideas in others have been applied in practical environments involving handwriting recognition, thinning methodologies, and multiple classifiers. Dr. Suen is the recipient of numerous awards, including the Gold Medal from the University of Bari (Italy 2012), the IAPR ICDAR Award (2005), the ITAC/NSERC national award (1992), and the "Concordia Lifetime Research Achievement" and "Concordia Fellow" Awards (2008 and 1998 respectively). He is a fellow of the IEEE (since 1986), IAPR (1994), and the Academy of Sciences of the Royal Society of Canada (1995). Currently, he is the Editor-in-Chief of the journal of Pattern Recognition and an Adviser or Associate Editor of 5 journals.
Beauty is one of the foremost ideas that define human personality. In this talk, various approaches to the comprehension and analysis of human beauty are presented and the use of these theories is outlined. Each set of theories is translated into a feature model that is tested for classification. Selecting the best set of features that result in the most accurate model for the representation of the human face is a key challenge. This research introduces the combined use of three main groups of features for classification of female facial beauty, to be used with classification through support vector machines. It concentrates on building an automatic system for the measurement of female facial beauty. The approach used is one of analyses of the central tenets of beauty, the successive application image processing techniques, and finally the usage of relevant machine learning methods to build an effective system for the automated assessment of facial beauty. Plenty of examples will be illustrated during the talk.