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Terrance E. Boult

Researcher at University of Colorado Colorado Springs

Publications -  285
Citations -  13190

Terrance E. Boult is an academic researcher from University of Colorado Colorado Springs. The author has contributed to research in topics: Facial recognition system & Biometrics. The author has an hindex of 51, co-authored 274 publications receiving 10853 citations. Previous affiliations of Terrance E. Boult include Columbia University & King Abdulaziz University.

Papers
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Journal ArticleDOI

Good recognition is non-metric

TL;DR: This review paper reconsiders the assumption of recognition as a pair-matching test, and introduces a new formal definition that captures the broader context of the problem, including how often metric properties are violated by recognition algorithms.
Book ChapterDOI

Component Fusion for Face Detection in the Presence of Heteroscedastic Noise

TL;DR: In this paper, the authors show that this uncertainty carries important information that, when exploited, leads to increased face localization accuracy, and discuss and compare possible solutions taking into account geometrical constraints.
Proceedings ArticleDOI

Unconstrained Face Detection and Open-Set Face Recognition Challenge.

TL;DR: In this paper, the authors address the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras, and show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates.
Posted Content

Good Recognition is Non-Metric

TL;DR: In this article, the authors consider multi-class training data and large sets of features in a learning context and make the case that locally metric algorithms should leverage outside information to solve the general recognition problem.
Proceedings ArticleDOI

Face and eye detection on hard datasets

TL;DR: This paper analyzes the detection and localization performance of the participating face and eye algorithms compared with the Viola Jones detector and four leading commercial face detectors and finds that the groups/companies focusing on long-range face detection outperform leading commercial applications.