Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 2001. Proceedings. IEEE ICCV Workshop on
Abstract
Recognising faces across multiple views is more challenging
than that from a fixed view because of the severe
non-linearity caused by rotation in depth, self-occlusion,
self-shading, and change of illumination. The problem
can be related to the problem of modelling the spatiotemporal
dynamics of moving faces from video input for
unconstrained live face recognition. Both problems remain
largely under-developed. To address the problems, a novel
approach is presented in this paper. A multi-view dynamic
face model is designed to extract the shape-and-pose-free
texture patterns of faces. The model provides a precise correspondence
to the task of recognition since the 3D shape
information is used to warp the multi-view faces onto the
model mean shape in frontal-view. The identity surface of
each subject is constructed in a discriminant feature space
from a sparse set of face texture patterns, or more practically,
from one or more learning sequences containing
the face of the subject. Instead of matching templates or
estimating multi-modal density functions, face recognition
can be performed by computing the pattern distances to the
identity surfaces or trajectory distances between the object
and model trajectories. Experimental results depict that this
approach provides an accurate recognition rate while using
trajectory distances achieves a more robust performance
since the trajectories encode the spatio-temporal information
and contain accumulated evidence about the moving
faces in a video input