Computer Science (School of) >
Computer Science >
Computer Science Theses >
Please use this identifier to cite or link to this item:
|Title: ||Facial feature detection and tracking with a 3D constrained local model|
|Authors: ||Yu, Meng|
|Issue Date: ||2010|
|Abstract: ||This thesis establishes a framework for facial feature detection and human
face movement tracking. Statistical models of shape and appearance are
built to represent the human face structure and interpret target images of
human faces. The approach is a patch-based method derived from an earlier
proposed method, the constrained local model (CLM)  algorithm.
In order to increase the ability to track face movements with large head
rotations, a 3D shape model is used in the system. And multiple texture
models from different viewpoints are used to model the appearance. During
fitting or tracking, the current estimate of pose (shape coordinates) is used
to select the appropriate texture model. The algorithm uses the shape model
and a texture model to generate a set of region template detectors. A search
is then performed in the global pose / shape space using these detectors.
Different optimisation frameworks are used in the implementation. The
training images are created by rendering expressive 3D face models with
different scales, rotations, expressions, brightness, etc.
Experimental results are demonstrated by fitting the model to image
sequences with large head rotations to evaluate the performance of the
algorithm. To evaluate the stability and selection of factors of the algorithm,
more experiments are carried out. The results show that the proposed 3D
constrained local model algorithm improves the performance of the original
CLM algorithm for videos with large out-of-plane head rotations.|
|Publisher: ||University of St Andrews|
|Appears in Collections:||Computer Science Theses|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.