Learning Components for Human Sensing

Monday, March 29, 2010 - 9:33pm


Tuesday, April 20, 2010
2:00 – 3:00
Computer Science Conference Room, Harold Frank Hall Rm. 1132

HOST: Matthew Turk

SPEAKER: Fernando De la Torre
Robotics Institute, Carnegie Mellon University

Title: Learning Components for Human Sensing


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, including our current work
on depression assessment from video, as well as hot-flash detection from
wearable sensors. In the second part of the talk I will show how several
extensions of the CA methods outperform state-of-the-art algorithms in
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.


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 the Human
Sensing Laboratory (http://humansensing.cs.cmu.edu).