Robotics Institute
Seminar, February 4
Time
and Place | Seminar Abstract | Speaker
Biography | Speaker Appointments
An Overview of Recent CMU Research on
Model-Based Face Processing
Simon Baker
Research Scientist
Time and Place |
Mauldin
Auditorium (NSH 1305)
Refreshments 3:15 pm
Talk 3:30 pm
A face
model is a mapping from a set of parameters to an image of a face. The most
well-known face models are Active Appearance Models and 3D Morphable
Models. Computer vision applications of face models include head pose
estimation for user interfaces, gaze estimation, pose normalization for face
recognition, lip-reading, expression recognition, and face coding for
low-bandwidth video-conferencing. In all of these applications, the key task is
to fit the face model to an input image; i.e. to find the parameters of the
model that match the input image as well as possible. Applying model fitting to
each image in a video in turn results in a non-rigid face tracking algorithm.
In
this talk I will describe how face model fitting, a non-linear optimization,
can be posed as an image alignment problem. Image alignment is a standard
computer vision technique, with applications to optical flow, tracking, mosaic
construction, layered scene representations, and medical image registration. I
will describe a new efficient image alignment algorithm and show how it relates
to others in a unifying framework. Applying our algorithm to faces results in
real-time 2D, 3D, and multi-view face model fitting algorithms.
I
will also describe some of our recent research on face model construction,
including automatic (unsupervised) model construction, model update, and 3D
model construction from 2D images.
Speaker Biography |
Simon Baker is a Research Scientist in the Robotics
Institute at
For appointments, please contact Simon Baker.
The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.