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
Nonnegative Discriminant Matrix Factorization
TL;DR: A novel method called nonnegative discriminant matrix factorization (NDMF) is proposed for image classification that integrates the nonnegative constraint, orthogonality, and discriminant information in the objective function and is proposed to enhance the discriminant ability of the learned base matrix.
Proceedings ArticleDOI
Combined subspace method using global and local features for face recognition
TL;DR: The experimental results show that the combined subspace method gives better recognition rate than other methods and is evaluated in view of the Bayes error, which shows how well samples can be classified.
Journal ArticleDOI
Scanning of own- versus other-race faces in infants from racially diverse or homogenous communities.
TL;DR: Experience in the community beyond the home appears to contribute to the development of differential scanning of own- versus other-race faces between 6 and 8 months of age.
Journal ArticleDOI
Gabor texture representation method for face recognition using the Gamma and generalized Gaussian models
Lei Yu,Zhongshi He,Qi Cao +2 more
TL;DR: The results show that the proposed GMTR-based and GPTR- based NLDA both significantly outperform the widely used Gabor features-based NLDA and other existing subspace methods.
Journal ArticleDOI
Generalized Robust Regression for Jointly Sparse Subspace Learning
TL;DR: The result indicates that GRR is a robust and efficient regression method for face recognition, by incorporating the elastic factor on the loss function, which can enhance the robustness to obtain more projections for feature selection or classification.
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.