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Face Recognition With Contiguous

Occlusion Using Markov Random Fields

Zihan Zhou, Andrew Wagner, Hossein Mobahi, John Wright, Yi Ma

2010/11/181 zw

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Outline

Introduction

Local spatial continuity

Error correction

Choosing parameters

Experiments

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Introduction

From ICCV09poster

Extended work from:

face recognition via sparse representation

Aimed at solving continuous error

Apply continuity constraint to sparse

representation

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Introduction

J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma.Robust face recognition via sparse representation. PAMI,

2009.

Sparse representation:

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y Ax

y Ax

=[ ]x

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Introduction

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y Ax e Solve this problem by:

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Introduction

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However, they only achieve their best performanceon occlusions that are not spatially correlated.

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Introduction

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Introduction

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Local Spatial Continuity

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support vector

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Local Spatial Continuity

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Local Spatial Continuity

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Approximation to the log-likelihood function

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Error correction

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Error correction

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1Estimating Linear Regressorx with Sparsity.

2Estimating Error Support s with MRF.

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Error correction

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3Identify the test image

assign it to the class that minimizes the error.1

l

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Choosing parameters

Choosing :

the level of error we would accept before

considering an entry of the image as occluded

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Choosing parameters

Choosing :

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Choosing parameters

Choosing :

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Choosing parameters

Choosing :

controls the strength of mutual interaction

between adjacent pixels

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Experiments

Recognition with synthetic occlusion

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Recognition with synthetic occlusion

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Experiments

Recognition with disguises

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Recognition with disguises

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Experiments

Validation on Extend Yale B dataset

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If

exceeds a thresholdit is declared as invalid

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First 19 subjects as training, the other 19subjects as invalid test images to be

rejected. IReceiver operating characteristic

(ROC) curves for 60% and 80% occlusion

Validation on Extend Yale B dataset

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Experiments

Experiments with realistic test images

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Training: 116 subjects with 38 illuminations.

ITesting:

855 images under realistic illuminations (indoors,outdoors),

which have been divided into five categories:

Normal: 354 images

Occlusion by eyeglasses: 118 images

Occlusion by sunglasses: 126 imagesOcclusion by hats: 40 images

Occlusion by various disguises: 217 images

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Validation on Extend Yale B dataset

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Validation on Extend Yale B dataset

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