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
Linear Laplacian Discrimination for Feature Extraction
TL;DR: The linear Laplacian discrimination (LLD) algorithm/or discriminant feature extraction is an extension of linear discriminant analysis (LDA) and the motivation is to address the issue that LDA cannot work well in cases where sample spaces are non-Euclidean.
Journal ArticleDOI
Weighted Sub-Gabor for face recognition
Loris Nanni,Dario Maio +1 more
TL;DR: A new face recognition approach based on the representation of each individual by a feature vector extracted through a bank of Gabor filters and Karhunen-Loeve transform to enhance the robustness to facial expression and illumination condition is introduced.
Proceedings ArticleDOI
Facial Feature Tracking Under Varying Facial Expressions and Face Poses Based on Restricted Boltzmann Machines
Yue Wu,Zuoguan Wang,Qiang Ji +2 more
TL;DR: Zhang et al. as discussed by the authors proposed a face shape prior model that is constructed based on the Restricted Boltzmann Machines (RBM) and their variants to capture the relationship between frontal face shapes and non-frontal face shapes.
Book ChapterDOI
Grassmann Registration Manifolds for Face Recognition
Yui Man Lui,J. Ross Beveridge +1 more
TL;DR: A novel method combining image perturbation and the geometry of manifolds is presented, which forms a tangent space from a set of perturbed images and observes that the tangentspace admits a vector space structure.
Proceedings ArticleDOI
Learning discriminative local binary patterns for face recognition
TL;DR: A simple method to learn discriminative LBPs in a supervised manner that represents an LBP-like descriptor as a set of pixel comparisons within a neighborhood and heuristically seeks for a setof pixel comparisons so as to maximize a Fisher separability criterion for the resulting histograms.
References
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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
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