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Multiview active shape models with SIFT descriptors

This thesis presents techniques for locating landmarks in images of human faces. A modified Active Shape Model (ASM [21]) is introduced that uses a form of SIFT descriptors [68]. Multivariate Adaptive Regression Splines (MARS [40]) are used to efficiently match descriptors around landmarks. This modified ASM is fast and performs well on frontal faces. The model is then extended to also handle non-frontal faces. This is done by first estimating the face's pose, rotating the face upright, then applying one of three ASM submodels specialized for frontal, left, or right three-quarter views. The multiview model is shown to be effective on a variety of datasets.

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/22867
Date January 2016
CreatorsMilborrow, Stephen
ContributorsNicolls, Fred C
PublisherUniversity of Cape Town, Faculty of Engineering and the Built Environment, Department of Electrical Engineering
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
TypeDoctoral Thesis, Doctoral, PhD
Formatapplication/pdf

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