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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Proceedings ArticleDOI
Boosting Local Feature Based Classifiers for Face Recognition
TL;DR: A cascade of strong classifiers are learned using bootstrapped negative examples, and the combination of classifiers based on two different types of features produces better results than using either type.
Journal ArticleDOI
Pose Invariant Face Recognition Using Probability Distribution Functions in Different Color Channels
TL;DR: A new and high performance pose invariant face recognition system based on the probability distribution functions (PDF) of pixels in different color channels is proposed and has been tested on the FERET and the Head Pose face databases.
Journal ArticleDOI
A linear discriminant analysis framework based on random subspace for face recognition
Xiaoxun Zhang,Yunde Jia +1 more
TL;DR: A novel framework, random discriminant analysis (RDA), is proposed to handle the small sample size problem when dealing with high-dimensional face data and can take full advantage of useful discriminant information in the face space.
Journal ArticleDOI
Use of one-dimensional iris signatures to rank iris pattern similarities
TL;DR: A one-dimensional approach to iris recognition that uses the Du measure as a matching mechanism, and generates a set of the most probable matches (ranks) instead of only the best match.
Journal ArticleDOI
Sparse Alignment for Robust Tensor Learning
TL;DR: A systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques is provided, thereby obtaining a general tensor alignment framework and a robust tensor learning method called sparse Tensor alignment (STA) is proposed for unsupervised tensor feature extraction.
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 by elastic bunch graph matching
TL;DR: A system for recognizing human faces from single images out of a large database containing one image per person, based on a Gabor wavelet transform, which is constructed from a small get of sample image graphs.
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.