Geof H. Givens

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Inexpensive “point-and-shoot” camera technology has combined with social network technology to give the general population a motivation to use face recognition technology. Users expect a lot; they want to snap pictures, shoot videos, upload, and have their friends, family and acquaintances more-or-less automatically recognized. Despite the(More)
The Good, the Bad, & the Ugly Face Challenge Problem was created to encourage the development of algorithms that are robust to recognition across changes that occur in still frontal faces. The Good, the Bad, & the Ugly consists of three partitions. The Good partition contains pairs of images that are considered easy to recognize. On the Good partition, the(More)
The FERET evaluation compared recognition rates for different semi-automated and automated face recognition algorithms. We extend FERET by considering when differences in recognition rates are statistically distinguishable subject to changes in test imagery. Nearest Neighbor classifiers using principal component and linear discriminant subspaces are(More)
The goal of the Multiple Biometrics Grand Challenge (MBGC) is to improve the performance of face and iris recognition technology from biometric samples acquired under unconstrained conditions. The MBGC is organized into three challenge problems. Each challenge problem relaxes the acquisition constraints in different directions. In the Portal Challenge(More)
We consider adaptive importance sampling techniques which use kernel density estimates at each iteration as importance sampling functions. These can provide more nearly constant importance weights and more precise estimates of quantities of interest than the SIR algorithm when the initial importance sampling function is di use relative to the target. We(More)
A statistical study is presented quantifying the effects of covariates such as gender, age, expression, image resolution and focus on three face recognition algorithms. Specifically, a Generalized Linear Mixed Effect model is used to relate probability of verification to subject and image covariates. The data and algorithms are selected from the Face(More)
We develop a general framework for analysing and testing genetic structure within a migratory assemblage that is based on measures of genetic differences between individuals. We demonstrate this method using microsatellite DNA data from the Bering-Chukchi-Beaufort stock of bowhead whales (Balaena mysticetus), sampled via Inuit hunting during the spring and(More)
Recognition difficulty is statistically linked to 11 subject covariate factors such as age and gender for three face recognition algorithms: principle components analysis, an interpersonal image difference classifier, and an elastic bunch graph matching algorithm. The covariates assess race, gender, age, glasses use, facial hair, bangs, mouth state,(More)
This paper reviews some of the major issues associated with the statistical evaluation of Human Identification algorithms, emphasizing comparisons between algorithms on the same set of sample images. A general notation is developed and common performance metrics are defined. A simple success/failure evaluation methodology where recognition rate depends upon(More)
This paper summarizes a study carried out on data from the Face Recognition Vendor Test 2006 (FRVT 2006). The finding of greatest practical importance is the discovery of a strong connection between a relatively simple measure of image quality and performance of state-of-the-art vendor algorithms in FRVT 2006. The image quality measure quantifies edge(More)