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: Automatic facial image analysis has been a long standing research
problem in computer vision. A key component in facial image analysis,
largely conditioning the success of subsequent algorithms (e.g. facial
expression recognition), is to define a vocabulary of possible dynamic
facial events. To date, that vocabulary has come from the anatomically
based Facial Action Coding System (FACS) or more subjective approaches
(i.e. emotion-specified expressions). The aim of this paper is to
discover facial events directly from video of naturally occurring facial
behavior, without recourse to FACS or other labeling schemes. To
discover facial events, we propose a temporal clustering algorithm,
Aligned Cluster Analysis (ACA), and a multi-subject correspondence
algorithm for matching expressions. We use a variety of video sources:
posed facial behavior (Cohn-Kanade database), unscripted facial behavior
(RU-FACS database) and some video in infants. Accuracy of (unsupervised)
ACA approached that of a supervised version, achieved moderate
intersystem agreement with FACS, and proved informative as a
visualization/summarization tool.