Representation and Recognition of Complex Human Motion
Abstract
Automatically recognizing complex human motions such as facial
expressions is a difficult task. It is beneficial to have
a simple and yet general representation on which to perform
classifications. We present a holistic
model-free representation of motion which uses the complete basis of
orthogonal Zernike polynomials. This representation is simple, yet general,
and can be used for describing many types of motion at many scales.
Starting from image sequences, locally smooth image velocities
are derived using a robust estimation procedure,
from which are computed compact representations of the flow using the Zernike basis.
Continuous density hidden Markov models are trained using the temporal sequences of
vectors thus obtained, and are used in subsequent classification tasks.
We present results of our method applied to image sequences of facial expressions
both with and without significant rigid head motion and to sequences of lip motion from
a known database. The results demonstrate its effectiveness at different classification
tasks.