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
Extended isomap for pattern classification
TL;DR: This paper presents an extended Isomap method that utilizes Fisher Linear Discriminant for pattern classification that shows promising results compared with best methods in the face recognition literature.
Journal ArticleDOI
Face Sketch Synthesis by Multidomain Adversarial Learning
TL;DR: This paper presents a novel face sketch synthesis method by multidomain adversarial learning (termed MDAL), which overcomes the defects of blurs and deformations toward high-quality synthesis.
Book
Recognition of Humans and Their Activities Using Video
TL;DR: The use of face and gait signatures for human identification and recognition of human activities from video sequences is discussed and the main challenge facing researchers is the development of recognition strategies that are robust to changes due to pose, illumination, disguise, and aging.
Proceedings ArticleDOI
Feature Selection Based on Linear Discriminant Analysis
TL;DR: The paper shows, for the first time, that it is feasible to employ LDA for feature selection and that different components statistically have different effects on the feature selection result, which can be evaluated by the components of the eigenvector.
Journal ArticleDOI
Sex differences in visual attention toward infant faces
TL;DR: In this paper, the authors examined whether one such cognitive mechanism is a visual attentional bias toward infant features, and if so, whether and how it is related to the sex of the adult and the adult's self-reported interest in infants.
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.