Journal ArticleDOI
Face Recognition Algorithms Surpass Humans Matching Faces Over Changes in Illumination
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TLDR
Seven state-of-the-art face recognition algorithms are compared with humans on a face-matching task and three algorithms surpassed human performance matching face pairs prescreened to be "difficult" and six algorithms surpassed humans on "easy" face pairs.Abstract:
There has been significant progress in improving the performance of computer-based face recognition algorithms over the last decade. Although algorithms have been tested and compared extensively with each other, there has been remarkably little work comparing the accuracy of computer-based face recognition systems with humans. We compared seven state-of-the-art face recognition algorithms with humans on a face-matching task. Humans and algorithms determined whether pairs of face images, taken under different illumination conditions, were pictures of the same person or of different people. Three algorithms surpassed human performance matching face pairs prescreened to be "difficult" and six algorithms surpassed humans on "easy" face pairs. Although illumination variation continues to challenge face recognition algorithms, current algorithms compete favorably with humans. The superior performance of the best algorithms over humans, in light of the absolute performance levels of the algorithms, underscores the need to compare algorithms with the best current control-humans.read more
Citations
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Book
Computer Vision: Algorithms and Applications
TL;DR: Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images and takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene.
Proceedings ArticleDOI
Attribute and simile classifiers for face verification
TL;DR: Two novel methods for face verification using binary classifiers trained to recognize the presence or absence of describable aspects of visual appearance and a new data set of real-world images of public figures acquired from the internet.
Book
Template Matching Techniques in Computer Vision: Theory and Practice
TL;DR: This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications.
Journal ArticleDOI
Describable Visual Attributes for Face Verification and Image Search
TL;DR: It is shown how one can create and label large data sets of real-world images to train classifiers which measure the presence, absence, or degree to which an attribute is expressed in images, which can then automatically label new images.
ReportDOI
FRVT 2006 and ICE 2006 large-scale results
P. Jonathon Phillips,W. Todd Scruggs,Alice J. O'Toole,Patrick J. Flynn,Kevin W Boyer,Cathy L. Schott,Matthew Sharpe +6 more
TL;DR: On the FRVT 2006 and the ICE 2006 datasets, recognition performance was comparable for all three biometrics and the best-performing face recognition algorithms were more accurate than humans.
References
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Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Journal ArticleDOI
Face recognition: A literature survey
TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
Journal ArticleDOI
The FERET evaluation methodology for face-recognition algorithms
TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Proceedings ArticleDOI
Overview of the face recognition grand challenge
P.J. Phillips,Patrick J. Flynn,T. Scruggs,Kevin W. Bowyer,Jin Chang,K. Hoffman,J. Marques,Jaesik Min,William J. Worek +8 more
TL;DR: The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images.