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An approach to automatic recognition of spontaneous facial actions
An Approach to Automatic Recognition of Spontaneous Facial Actions
B. Braathen, M.S. Bartlett, G. Littlewort, E. Smith, and J.R. Movellan
Institute for Neural Computation
University of California, San Diego
Email: bjorn, marni, gwen, evan, javier @inc.ucsd.edu
Abstract
We present ongoing work on a project for automatic
recognition of spontaneous facial actions. Spontaneous fa-
cial expressions differ substantially from posed expressions,
similar to how spontaneous speech differs from directed
speech. Previous methods for automatic facial expression
recognition assumed images were collected in controlled
environments in which the subjects deliberately faced the
camera. Since people often nod or turn their heads, auto-
matic recognition of spontaneous facial behavior requires
methods for handling out-of-image-plane head rotations.
There are many promising approaches to address the prob-
lem of out-of-image plane rotations. In this paper we ex-
plore an approach based on 3-D warping of images into
canonical views. A front-end system was developed that
jointly estimates camera parameters, head geometry and 3-
D head pose across entire sequences of video images. First
a small set of images was used to estimate camera param-
eters and 3D face geometry. Markov chain Monte-Carlo
methods were then used to recover the most likely sequence
of 3D poses given a sequence of video images. Once the 3D
pose was known, we warped each image into frontal views
with a canonical face geometry. We evaluated the perfor-
mance of the approach as a front-end for a spontaneous ex-
pression recognition system using support vector machines
and hidden Markov models. This system employed general
purpose learning mechanisms that can be applied to recog-
nition of any facial movement. We showed that 3D tracking
and warping followed by machine learning techniques di-
rectly applied to the warped images, is a viable and promis-
ing technology for automatic facial expression recognition.
One exciting
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