Journal ArticleDOI
The FERET evaluation methodology for face-recognition algorithms
TLDR
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.Abstract:
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. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.read more
Citations
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Book ChapterDOI
Face De-identification
TL;DR: This chapter describes a novel framework for the de-identification of face images using multi-factor models which unify linear, bilinear, and quadratic data models, and shows in experiments that the new algorithm is able to protect privacy while preserving data utility.
Book ChapterDOI
Evaluation Methods in Face Recognition
TL;DR: This chapter focuses on describing the FERET and FRVT 2002 protocols, which served as a basis for the FRVT 2006 and MBE 2010 evaluations.
Journal ArticleDOI
A literature survey on robust and efficient eye localization in real-life scenarios
TL;DR: This paper presents a detailed review of prominent algorithms from the perspective of learning generalizable, flexible and efficient statistical eye models from a small number of training images, and organizes the discussion of the global aspects of eye localization in uncontrolled environments towards the development of a robust eye localization system.
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Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture
TL;DR: In this article, a couple mappings method for low-resolution face recognition using deep convolutional neural networks (DCNNs) is proposed, which consists of two branches of DCNNs to map the high and low resolution face images into a common space with nonlinear transformations.
Book
Object Categorization
TL;DR: In this article, the authors present foundations, original research and trends in the field of object categorization by computer vision methods, including patch-based methods, boundary fragment-based models and geometric modeling of 2D spatial relations between parts.
References
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Journal ArticleDOI
Using discriminant eigenfeatures for image retrieval
D.L. Swets,Juyang Weng +1 more
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Baback Moghaddam,Alex Pentland +1 more
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