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.