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
Misleading First Impressions Different for Different Facial Images of the Same Person
TL;DR: It is shown that images of the same individual can lead to different impressions, with within-individual image variance comparable to or exceeding between-individuals variance for a variety of social judgments.
Journal ArticleDOI
A Pyramidal Neural Network For Visual Pattern Recognition
TL;DR: This paper applies PyraNet to determine gender from a facial image, and compares its performance on the standard facial recognition technology (FERET) database with three classifiers: The convolutional neural network (NN), the k-nearest neighbor (k-NN), and the support vector machine (SVM).
Journal ArticleDOI
Local Color Vector Binary Patterns From Multichannel Face Images for Face Recognition
TL;DR: Experimental results show that the proposed LCVBP feature is able to yield excellent FR performance for challenging face images, and has successfully been tested by comparing other state-of-the-art face descriptors.
Journal ArticleDOI
Gabor Ordinal Measures for Face Recognition
TL;DR: This paper proposes a novel facial feature extraction method named Gabor ordinal measures (GOM), which integrates the distinctiveness of Gabor features and the robustness of Ordinal measures as a promising solution to jointly handle inter-person similarity and intra-person variations in face images.
Proceedings ArticleDOI
Generic Face Alignment using Boosted Appearance Model
TL;DR: A discriminative framework for efficiently aligning images that greatly improves the robustness, accuracy and efficiency of face alignment by a large margin, especially for unseen data.
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.