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
Normalization of Face Illumination Based on Large-and Small-Scale Features
TL;DR: It is argued that both large-and small-scale features of a face image are important for face restoration and recognition, and it is suggested that illumination normalization should be performed mainly on the large-scale featured rather than on the original face image.
Journal ArticleDOI
Gender recognition: A multiscale decision fusion approach
TL;DR: This paper presents an approach to gender recognition based on shape, texture and plain intensity features gathered at different scales and proposes a new dataset for gender evaluation based on images from the UND database.
Proceedings Article
PCA vs. ICA: A Comparison on the FERET Data Set.
TL;DR: Testing on the FERET data set and using standard partitions, it is found that, when a proper distance metric is used, PCA significantly outperforms ICA on a human face recognition task, contrary to previously published results.
Journal ArticleDOI
Coupled Bias–Variance Tradeoff for Cross-Pose Face Recognition
Annan Li,Shiguang Shan,Wen Gao +2 more
TL;DR: It is found that striking a coupled balance between bias and variance in regression for different poses could improve the regression-based cross-pose face representation, i.e., the regressor can be more stable against a pose difference.
Journal ArticleDOI
Using the idea of the sparse representation to perform coarse-to-fine face recognition
TL;DR: A coarse-to-fine face recognition method that identifies the classes that are ''far'' from the test sample and removes them from the set of the training samples and the classification problem becomes a simpler one with fewer classes.
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.