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William A. P. Smith

Researcher at University of York

Publications -  202
Citations -  5631

William A. P. Smith is an academic researcher from University of York. The author has contributed to research in topics: Statistical model & Facial recognition system. The author has an hindex of 35, co-authored 198 publications receiving 4489 citations. Previous affiliations of William A. P. Smith include Imperial College London & Daresbury Laboratory.

Papers
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Journal ArticleDOI

Antibiotic activity of valinomycin Molecular dynamicssimulations involving the water/membrane interface

TL;DR: In this article, molecular dynamics simulations, covering 550 ps of equilibrium and 100 ps of production, of the adsorption of the inhibitor valinomycin and the dissociation reaction of its potassium complex at the two interfaces of a hydrophobic membrane bounded by water, are reported.
Proceedings ArticleDOI

Shape-from-shading driven 3D Morphable Models for Illumination Insensitive Face Recognition

TL;DR: A method for face shape and albedo estimation which uses a morphable model in conjunction with non-Lambertian shape-from-shading to construct a spherical harmonic basis which can be used generatively to model face appearance variation under arbitrarily complex illumination.
Proceedings ArticleDOI

Inverse Rendering with a Morphable Model: A Multilinear Approach.

TL;DR: A complete framework to inverse render faces from single images using a 3D Morphable Model (3DMM) and incorporates features like edges and specular highlights into the cost function is presented.
Journal ArticleDOI

Evaluation of structure-from-motion for analysis of small-scale glacier dynamics

TL;DR: In this article, a measurement procedure for marineterminating glaciers using structure-from-motion including proper survey planning, control point design and model alignment is proposed, which is effective for documentation of small-scale glacier dynamics, show the importance of appropriate alignment strategies for models with poorly distributed control points and compare two SfM tools (Agisoft Metashape and Bentley ContextCapture) concluding that ContextCapture offers around 17% lower error, 25% faster processing and better reconstruction of fine details and shadowed concavities.
Book ChapterDOI

Learning Mixture Models for Gender Classification Based on Facial Surface Normals

TL;DR: The aim in this paper is to show how to discriminate gender using a parameterized representation of fields of facial surface normals (needle-maps) using principle geodesic analysis (PGA) to parameterize the facial needle-maps.