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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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Proceedings ArticleDOI

Images can be regenerated from quantized biometric match score data

TL;DR: It is shown that it is still possible to regenerate biometric images even if biometric algorithms emit only quantized match scores, and it is concluded that the quantization of match score values does not protect against the regeneration of images from stored biometric data.
Journal ArticleDOI

Coupled Discriminative Feature Learning for Heterogeneous Face Recognition

TL;DR: Experimental results on three different heterogeneous face recognition applications show the effectiveness of the proposed CDFL approach, which directly learns discriminative features from raw pixels for face representation.
Journal ArticleDOI

Multi-objective optimization for modular granular neural networks applied to pattern recognition

TL;DR: The proposed method aims at finding non-dominated solutions based on the number of data points for training and the recognition error using a multi-objective approach and can be used in different areas of application, such as human recognition, classification problems or time series prediction.
Journal ArticleDOI

A robust eye detection method using combined binary edge and intensity information

TL;DR: An improved face region extraction algorithm and a light dots detection algorithm are proposed for better eye detection performance.
Proceedings ArticleDOI

Genealogical face recognition based on UB KinFace database

TL;DR: A challenging problem raised in biometric recently, genealogical face recognition, and metric learning and transfer subspace learning are adopted to abridge the great discrepancy between children and their old parents.
References
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Journal ArticleDOI

Eigenfaces for recognition

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

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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