Overview of the Multiple Biometrics Grand Challenge
P. Jonathon Phillips,Patrick J. Flynn,J. Ross Beveridge,W. Todd Scruggs,Alice J. O'Toole,David S. Bolme,Kevin W. Bowyer,Bruce A. Draper,Geof H. Givens,Yui Man Lui,Hassan Sahibzada,Joseph A. Scallan,Samuel Weimer +12 more
- pp 705-714
TLDR
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.Abstract:
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 Problem, the goal is to recognize people from near-infrared (NIR) and high definition (HD) video as they walk through a portal. Iris recognition can be performed from the NIR video and face recognition from the HD video. The availability of NIR and HD modalities allows for the development of fusion algorithms. The Still Face Challenge Problem has two primary goals. The first is to improve recognition performance from frontal and off angle still face images taken under uncontrolled indoor and outdoor lighting. The second is to improve recognition performance on still frontal face images that have been resized and compressed, as is required for electronic passports. In the Video Challenge Problem, the goal is to recognize people from video in unconstrained environments. The video is unconstrained in pose, illumination, and camera angle. All three challenge problems include a large data set, experiment descriptions, ground truth, and scoring code.read more
Citations
More filters
Journal ArticleDOI
Secure and Robust Iris Recognition Using Random Projections and Sparse Representations
TL;DR: This paper proposes a unified framework based on random projections and sparse representations that can simultaneously address all three issues mentioned above in relation to iris biometrics, and includes enhancements to privacy and security by providing ways to create cancelable iris templates.
Journal ArticleDOI
Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection
TL;DR: It is shown that the proposed approach boosts the likelihood of correctly identifying the person of interest through the use of different fusion schemes, 3-D face models, and incorporation of quality measures for fusion and video frame selection.
BookDOI
Handbook of Iris Recognition
Kevin W. Bowyer,Mark J. Burge +1 more
TL;DR: This second edition of this comprehensive handbook presents a broad overview of the state of the art in this exciting and rapidly evolving field, and describes open source software for the iris recognition pipeline and datasets of iris images.
Proceedings ArticleDOI
An introduction to the good, the bad, & the ugly face recognition challenge problem
P. Jonathon Phillips,J. Ross Beveridge,Bruce A. Draper,Geof H. Givens,Alice J. O'Toole,David S. Bolme,Joseph Dunlop,Yui Man Lui,Hassan Sahibzada,Samuel Weimer +9 more
TL;DR: 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.
Book ChapterDOI
Dictionary-based face recognition from video
TL;DR: This work introduces the concept of video-dictionaries for face recognition, which generalizes the work in sparse representation and dictionaries for faces in still images and performs significantly better than many competitive video-based face recognition algorithms.
References
More filters
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.
Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
TL;DR: The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life, and exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background.
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.
Journal ArticleDOI
The FERET database and evaluation procedure for face-recognition algorithms
TL;DR: The FERET evaluation procedure is an independently administered test of face-recognition algorithms to allow a direct comparison between different algorithms and to assess the state of the art in face recognition.