Face recognition vendor test 2002

Phillips, P. Jonathon; Grother, Patrick J.; Micheals, Ross J.; Blackburn, Duane M.; Tabassi, Elham

doi:10.1109/amfg.2003.1240822

Cited by 269 publications

(212 citation statements)

References 12 publications

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“…The availability of numerous commercial face recognition systems [1] attests to the significant progress achieved in the research field [2]. Despite these achievements, face recognition continues to be an active topic in computer vision research.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition with Local Binary Patterns

Ahonen

Hadid

Pietikäinen

2004

Lecture Notes in Computer Science

1,773

1,162

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Abstract. In this work, we present a novel approach to face recognition which considers both shape and texture information to represent face images. The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single, spatially enhanced feature histogram efficiently representing the face image. The recognition is performed using a nearest neighbour classifier in the computed feature space with Chi square as a dissimilarity measure. Extensive experiments clearly show the superiority of the proposed scheme over all considered methods (PCA, Bayesian Intra/extrapersonal Classifier and Elastic Bunch Graph Matching) on FERET tests which include testing the robustness of the method against different facial expressions, lighting and aging of the subjects. In addition to its efficiency, the simplicity of the proposed method allows for very fast feature extraction.

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Section: Introductionmentioning

confidence: 99%

Face Recognition with Local Binary Patterns

Ahonen

Hadid

Pietikäinen

2004

Lecture Notes in Computer Science

1,773

1,162

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“…Indeed, in large independent US government tests of the leading algorithms (e.g., in FERET [10] and FRVT [9]) improper handling of variable (outdoor) lighting has been a key limiting factor in achieving recognition rates obtained in more controlled laboratory conditions.…”

Section: Introductionmentioning

confidence: 99%

A Practical Face Relighting Method for Directional Lighting Normalization

Lee

Moghaddam

2005

Lecture Notes in Computer Science

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We propose a simplified and practical computational technique for estimating directional lighting in uncalibrated images of faces in frontal pose. We show that this inverse problem can be solved using constrained least-squares and class-specific priors on shape and reflectance. For simplicity, the principal illuminant is modeled as a mixture of Lambertian and ambient components. By using a generic 3D face shape and an average 2D albedo we can efficiently compute the directional lighting with surprising accuracy (in real-time and with or without shadows). We then use our lighting direction estimate in a forward rendering step torelightärbitrarily-lit input faces to a canonical (diffuse) form as needed for illumination-invariant face verification. Experimental results with the Yale Face Database B as well as real access-control datasets illustrate the advantages over existing pre-processing techniques such as a linear ramp (facet) model commonly used for lighting normalization.This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract. We propose a simplified and practical computational technique for estimating directional lighting in uncalibrated images of faces in frontal pose. We show that this inverse problem can be solved using constrained least-squares and class-specific priors on shape and reflectance. For simplicity, the principal illuminant is modeled as a mixture of Lambertian and ambient components. By using a generic 3D face shape and an average 2D albedo we can efficiently compute the directional lighting with surprising accuracy (in real-time and with or without shadows). We then use our lighting direction estimate in a forward rendering step to "relight" arbitrarily-lit input faces to a canonical (diffuse) form as needed for illumination-invariant face verification. Experimental results with the Yale Face Database B as well as real access-control datasets illustrate the advantages over existing pre-processing techniques such as a linear ramp (facet) model commonly used for lighting normalization.

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“…Within this context, we can define two specific tasks: authentication and identification [9]: the former aims to verify the identity declared by a subject on the basis of some biometric characteristics, the latter aims to recognize a person who does not declare his/her identity, but who is assumed to be one of the persons which constitute a referring gallery. Faces can be biometrically characterized in the same way for these two tasks; what differs is the comparison criterion: in the case of authentication, it is necessary to define an absolute threshold, while in the case of identification the identity of the test face is determined as the one of the gallery image which has the best match with the test itself.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Feature Based Face Authentication System,

et al. 2006

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Abstract. In this paper a fully automatic face verification system is presented. A face is characterized by a vector (jet) of coefficients determined applying a bank of Gabor filters in correspondence to 19 facial fiducial points automatically localized. The identity claimed by a subject is accepted or rejected depending on a similarity measure computed among the jet characterizing the subject, and the ones corresponding to the subjects in the gallery. The performance of the system has been quantified according to the Lausanne evaluation protocol for authentication.

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Face recognition vendor test 2002

Abstract: The Face Recognition Vendor Test (FRVT)

Cited by 269 publications

References 12 publications

Face Recognition with Local Binary Patterns

Face Recognition with Local Binary Patterns

A Practical Face Relighting Method for Directional Lighting Normalization

An Automatic Feature Based Face Authentication System,

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