Hi Gang,
This week we will return to our regularly schedule tea talks with a talk by David Rim. Title and abstract are below. Hope to see you there.
Time: 14h00, Thursday August 18th
Location: LISA lab (AA3256)
Title: Learning Identity-Invariant Features for Expression Recognition
with 3D CGI Models for Semi-Supervised Learning
Abstract:
I plan to propose a method for learning features for expression
recognition by leveraging work done in the graphics community for
modeling and generating realistic human faces and related
motion-capture technology. I will demonstrate that these models
provide expression representations which are far more compact and more
importantly, easier to interpret and code than Facial Action Coding
System Action Units. I will demonstrate a labeling process which I
believe will lead to more accurate labels, and show initial
experiments using simple linear models for feature learning.
I will propose a convolutional network model to learn better features
(filter maps) across models for different "identities" as well as
using unlabeled human faces. Finally, I will propose a semi-supervised
model which can use these features to learn an inductive function
which maps 2D facial images to the expression representation using
these learned features inductively. I hope a useful discussion will
follow.
Cheers,
Aaron
--
Aaron C. Courville
Département d’Informatique et
de recherche opérationnelle
Université de Montréal
email:Aaron.Courville@gmail.com