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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.

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Citations
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Journal ArticleDOI

Multi-view face and eye detection using discriminant features

TL;DR: The RNDA relaxes Gaussian assumptions of Fisher discriminant analysis (FDA), and it can handle more general class distributions, and it improves the traditional nonparametric discriminantAnalysis (NDA) by alleviating its computational complexity.
Proceedings ArticleDOI

Regression and classification approaches to eye localization in face images

TL;DR: In this paper, a regression approach aiming to directly minimize errors in the predicted eye positions, a simple Bayesian model of eye and non-eye appearance, and a discriminative eye detector trained using AdaBoost are investigated.
Book ChapterDOI

View-invariant Estimation of Height and Stride for Gait Recognition

TL;DR: A parametric method to automatically identify people in monocular low-resolution video by estimating the height and stride parameters of their walking gait, which are functions of body height, weight, and gender.
Proceedings ArticleDOI

Unified subspace analysis for face recognition

TL;DR: A unified subspace analysis method is developed that achieves better recognition performance than the standard subspace methods on over 2000 face images from the FERET database.
Proceedings ArticleDOI

Silhouette transformation based on walking speed for gait identification

TL;DR: A method of gait silhouette transformation from one speed to another to cope with walking speed changes in gait identification and silhouettes are restored by combining the unchanged static features and the transformed dynamic features.
References
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Journal ArticleDOI

Eigenfaces for recognition

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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

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

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.
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