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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
Paulina Lewińska,Paulina Lewińska,Oskar Glowacki,Oskar Glowacki,Mateusz Moskalik,William A. P. Smith +5 more
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.