Locating facial features with active shape models
type: CreativeWork
description: This dissertation focuses on the problem of locating features in frontal views of upright human faces. The dissertation starts with the Active Shape Model of Cootes et al. [19] and extends it with the following techniques: 1. Selectively using two-instead of one-dimensional landmark profiles. 2. Stacking two Active Shape Models in series. 3. Extending the set of landmarks. 4. Trimming covariance matrices by setting most entries to zero. 5. Using other modifications such as adding noise to the training set. The resulting feature locater is shown to compare favorably with previously published methods.
abstract: This dissertation focuses on the problem of locating features in frontal views of upright human faces. The dissertation starts with the Active Shape Model of Cootes et al. [19] and extends it with the following techniques: 1. Selectively using two-instead of one-dimensional landmark profiles. 2. Stacking two Active Shape Models in series. 3. Extending the set of landmarks. 4. Trimming covariance matrices by setting most entries to zero. 5. Using other modifications such as adding noise to the training set. The resulting feature locater is shown to compare favorably with previously published methods.
author: Milborrow, Stephen
language: eng
date published: 2007
date modified: 2026-07-22T22:22:41Z
provider: University of Cape Town
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Department of Electrical Engineering

PropertyValue
property ID: Handle
value: http://hdl.handle.net/11427/5161