J
John D. Bustard
Researcher at University of Southampton
Publications - 17
Citations - 328
John D. Bustard is an academic researcher from University of Southampton. The author has contributed to research in topics: Biometrics & Spoofing attack. The author has an hindex of 9, co-authored 17 publications receiving 299 citations. Previous affiliations of John D. Bustard include Queen's University & Queen's University Belfast.
Papers
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Journal ArticleDOI
Toward Unconstrained Ear Recognition From Two-Dimensional Images
John D. Bustard,Mark S. Nixon +1 more
TL;DR: The purpose of this paper is to suggest how progress toward robustness might be achieved through a technique that improves ear registration, and focuses on 2-D images, treating the ear as a planar surface that is registered to a gallery using a homography transform calculated from scale-invariant feature-transform feature matches.
Proceedings ArticleDOI
Robust 2D Ear Registration and Recognition Based on SIFT Point Matching
John D. Bustard,Mark S. Nixon +1 more
TL;DR: A new technique is described which improves the robustness of ear registration and recognition, addressing issues of pose variation, background clutter and occlusion.
Proceedings Article
Can gait biometrics be Spoofed
Abdenour Hadid,Mohammad Ghahramani,Vili Kellokumpu,Matti Pietikäinen,John D. Bustard,Mark S. Nixon +5 more
TL;DR: This paper provides the first investigation in the research literature on how clothing can be used to spoof a target and evaluates the performance of two state-of-the-art recognition methods on a novel gait spoofing database recorded at the University of Southampton.
Proceedings ArticleDOI
3D morphable model construction for robust ear and face recognition
John D. Bustard,Mark S. Nixon +1 more
TL;DR: This hypothesis that using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views is investigated using an approach based on the construction of a 3D morphable model of the head and ear.
Book ChapterDOI
On Acquisition and Analysis of a Dataset Comprising of Gait, Ear and Semantic data
TL;DR: This chapter is the first description of the multibiometric data acquired using the University of Southampton's Multi-Biometric Tunnel; a biometric portal using automatic gait, face and ear recognition for identification purposes and is ideal for use in high throughput security scenarios and for the collection of large datasets.