Fernando De la Torre Gives a Seminar on Component Analysis for Human Sensing

Date: February 14, 2013
Place: ISR's meeting room, Instituto Superior Técnico of the Universidade Técnica de Lisboa, Portugal

Enabling computers to understand human behavior has the potential to revolutionize many areas that benefit society such as clinical diagnosis, human computer interaction, and social robotics. A critical element in the design of any behavioral sensing system is to find a good representation of the data for encoding, segmenting, classifying and predicting subtle human behavior. In this talk I will propose several extensions of Component Analysis (CA) techniques (e.g., kernel principal component analysis, support vector machines, spectral clustering) that are able to learn spatio-temporal representations or components useful in many human sensing tasks. In particular, I will show how several extensions of CA methods outperform state-of-the-art algorithms in problems such as facial feature detection and tracking, temporal clustering and labeling of human behavior, early detection of activities, and robust classification. The talk will be adaptive, and I will discuss the topics of major interest to the audience.

Fernando De la Torre received his B.Sc. degree in Telecommunications (1994), M.Sc. (1996), and Ph. D. (2002) degrees in Electronic Engineering from La Salle School of Engineering in Ramon Llull University, Barcelona, Spain. In 2003 he joined the Robotics Institute at Carnegie Mellon University , and since 2010 he has been a Research Associate Professor. Dr. De la Torre's research interests include computer vision and machine learning, in particular face analysis, optimization and component analysis methods, and its applications to human sensing. He is Associated Editor at IEEE PAMI and he won the best student paper award in IEEE CVPR-2012. Currently he leads the Component Analysis Laboratory (http://ca.cs.cmu.edu) and the Human Sensing Laboratory (http://humansensing.cs.cmu.edu).