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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Journal ArticleDOI
Face Recognition With an Improved Interval Type-2 Fuzzy Logic Sugeno Integral and Modular Neural Networks
TL;DR: Simulation results show that the interval type-2 Sugeno integral is able to improve the recognition rate for the benchmark face databases and could also be a useful tool in other areas of applications.
LDA - SSS package: supplement material for "linear discriminant analysis for the small sample size problem: an overview"
TL;DR: The type, characteristics and taxonomy of these methods which can overcome SSS problem are covered and some important datasets and software/packages are highlighted.
Journal ArticleDOI
Face Recognition by Computers and Humans
TL;DR: The study of how humans perceive faces can be used to help design practical systems for face recognition, including recognition from unconstrained video sequences, incorporating familiarity into algorithms, modeling effects of aging, and developing biologically plausible models for human face recognition ability are discussed.
Journal ArticleDOI
Age estimation via face images: a survey
TL;DR: A thorough analysis of recent research in aging and age estimation is presented; popular algorithms used in age estimation, existing models, and how they compare with each other are discussed; performance of various systems andHow they are evaluated are compared.
Journal ArticleDOI
Learning Robust and Discriminative Subspace With Low-Rank Constraints
TL;DR: This paper presents a discriminative subspace learning method called the supervised regularization-based robust subspace (SRRS) approach, by incorporating the low-rank constraint, which learns a low-dimensional subspace from recovered data, and explicitly incorporates the supervised information.
References
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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.
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Face recognition by elastic bunch graph matching
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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.
Journal ArticleDOI
Using discriminant eigenfeatures for image retrieval
D.L. Swets,Juyang Weng +1 more
TL;DR: This paper describes the automatic selection of features from an image training set using the theories of multidimensional discriminant analysis and the associated optimal linear projection, and demonstrates the effectiveness of these most discriminating features for view-based class retrieval from a large database of widely varying real-world objects.
Journal ArticleDOI
Probabilistic visual learning for object representation
Baback Moghaddam,Alex Pentland +1 more
TL;DR: An unsupervised technique for visual learning is presented, which is based on density estimation in high-dimensional spaces using an eigenspace decomposition and is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and nonrigid objects.