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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

Heterogeneous image transformation

TL;DR: In the proposed model, SFS selects nearest neighbors adaptively based on sparse representation to implement an initial transformation, and subsequently the SVR model is applied to estimate the lost high frequency information or detail information.
Journal ArticleDOI

Racial stereotypes and interracial attraction: phenotypic prototypicality and perceived attractiveness of Asians.

TL;DR: It is suggested that for Whites, attraction to Asians may be based, in part, on stereotypes and variations in Asians' racial appearance, which inform theory on how within-group variation in racial appearance affects stereotyping and other social outcomes.
Journal ArticleDOI

Learning Rotation-Invariant Local Binary Descriptor

TL;DR: Extensive experimental results on four different visual recognition tasks, including image patch matching, texture classification, face recognition, and scene classification, show that the RI-LBD and TRICo-L BD outperform most existing local descriptors.
Proceedings Article

Gender recognition from face images with local WLD descriptor

TL;DR: This paper investigates Weber's Local Descriptor (WLD) for gender recognition by introducing local spatial information; divide an image into a number of blocks, calculate WLD descriptor for each block and concatenate them and reports the best combination of these parameters.
Proceedings ArticleDOI

MVF-Net: Multi-View 3D Face Morphable Model Regression

TL;DR: In this paper, a self-supervised view alignment loss is proposed to regress 3DMM parameters from multi-view inputs with an end-to-end trainable Convolutional Neural Network.
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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