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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 apparent(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)
— 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,(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)
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)
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 clas-sifiers using principal component and linear discriminant subspaces are(More)
Recognition difficulty is statistically linked to 11 subject co-variate factors such as age and gender for three face recognition algorithms: principle components analysis, an inter-personal image difference classifier, and an elastic bunch graph matching algorithm. The covariates assess race, gender , age, glasses use, facial hair, bangs, mouth state,(More)
— In face recognition, quality is typically thought of as a property of individual images, not image pairs. The implicit assumption is that high-quality images should be easy to match to each other, while low quality images should be hard to match. This paper presents a relational graph-based evaluation technique that uses match scores produced by face(More)
Some people's faces are easier to recognize than others, but it is not obvious what subject-specific factors make individual faces easy or difficult to recognize. This study considers 11 factors that might make recognition easy or difficult for 1¡ 072 human subjects in the FERET dataset. The specific factors are: race (white, Asian, African-American, or(More)
Recent studies show that face recognition in uncontrolled images remains a challenging problem, although the reasons why are less clear. Changes in illumination are one possible explanation, even though algorithms developed since the advent of the PIE and Yale B data bases supposedly compensate for illumination variation. Edge density has also been shown to(More)