Event Location: NSH 1507
Bio: 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 1997 and 2000 he was an Assistant and
Associate Professor in the Department of Communications and Signal
Theory in Enginyeria La Salle. Since 2005 he has been a Research
Assistant Professor in the Robotics Institute at Carnegie Mellon
University. 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. Dr.
De la Torre co-organized the first workshop on component analysis
methods for modeling, classification and clustering problems in
computer vision in conjunction with CVPR’07, and the workshop on human
sensing from video jointly with CVPR’06. He has also given several
tutorials at international conferences (ECCV’06, CVPR’06, ICME’07,
ICPR’08) on the use and extensions of component analysis methods.
Currently he leads the Component Analysis Laboratory
(http://ca.cs.cmu.edu) and co-leads the Human Sensing Laboratory
(http://humansensing.cs.cmu.edu).

Abstract: Providing computers with the ability to understand human behavior from
sensory data (e.g. video, audio, or wearable sensors) is an essential
part of many applications that can 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, and spectral clustering)
that are able to learn spatio-temporal representations or components
useful in many human sensing tasks.

In the first part of the talk, I will give an overview of several
ongoing projects in the CMU Human Sensing Laboratory. In the second part
of the talk, I will show how several extensions of the CA methods
outperform state-of-the-art algorithms in human sensing problems such as
temporal alignment of human behavior, temporal segmentation/clustering
of human activities, joint segmentation and classification of human
behavior, and facial feature detection in images. The talk will be
adaptive, and I will discuss the topics of major interest to the audience.