Face Recognition Under Varying Illumination Erald VUÇINI Vienna University of Technology Muhittin...
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![Page 1: Face Recognition Under Varying Illumination Erald VUÇINI Vienna University of Technology Muhittin GÖKMEN Istanbul Technical University Eduard GRÖLLER Vienna.](https://reader036.fdocuments.net/reader036/viewer/2022062407/56649d365503460f94a0db4c/html5/thumbnails/1.jpg)
Face RecognitionFace Recognition Under Under Varying IlluminationVarying Illumination
Erald VUÇINI Vienna University of Technology
Muhittin GÖKMEN Istanbul Technical University
Eduard GRÖLLER Vienna University of Technology
![Page 2: Face Recognition Under Varying Illumination Erald VUÇINI Vienna University of Technology Muhittin GÖKMEN Istanbul Technical University Eduard GRÖLLER Vienna.](https://reader036.fdocuments.net/reader036/viewer/2022062407/56649d365503460f94a0db4c/html5/thumbnails/2.jpg)
Erald Vuçini - Vienna University of Technology 2
Face Recognition System
Image Capture
Face Identification
Face Detection
Database
Feature Projection
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Erald Vuçini - Vienna University of Technology 3
Face Recognition Approaches
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Local Feature Analysis
Active Appearance Model
Hidden Markov Model
Support Vector Machine
…
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Erald Vuçini - Vienna University of Technology 4
Face Recognition – Problems
The variations of the same face due toilluminationviewing direction
are almost always larger than image variations due to changes in the face identity
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Erald Vuçini - Vienna University of Technology 5
Handling Variable Illumination
Extract illumination invariant features
Transform variable illumination to canonical representation
Model 2D illumination variations
Utilize 3D face models whose shapes and albedos are obtained in advance
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Erald Vuçini - Vienna University of Technology 6
I. Dimensionality Reduction - LDA better than PCA regarding illumination
II. Image Synthesis - Solve the Small Sample Size (SSS) problem
III. Reconstruction – Restore frontal illumination
Outline of proposed approach
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Erald Vuçini - Vienna University of Technology 7
I. Dimensionality Reduction - LDA better than PCA regarding illumination
II. Image Synthesis - Solve the Small Sample Size (SSS) problem
III. Reconstruction – Restore frontal illumination
Dimensionality Reduction
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Erald Vuçini - Vienna University of Technology 8
Principal Component Analysis (PCA)
One of the most commonly used methods in Face Recognition
Maximizes the scattering of all projected samples
PCAx1
x2
x1
x2z1
z 2
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Erald Vuçini - Vienna University of Technology 9
PCA under Varying Illumination
PCA fails with variant illumination
The scatter being maximized is due to
Between-class scatter
Within-class scatter
Discard 3 most significant principal components to reduce lighting variation
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Erald Vuçini - Vienna University of Technology 10
LDA Interpretation
LDA is a class specific method
LDA
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Erald Vuçini - Vienna University of Technology 11
LDA Problems
LDA maximizes the ratio of Between-class scatter and Within-class Scatter
Within-class Scatter singularity problem
Fisher LDA (FLDA) removes Null Space
FLDA handles best the variation in lighting, having lower error rate than PCA
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Erald Vuçini - Vienna University of Technology 12
I. Dimensionality Reduction - LDA better than PCA regarding illumination
II. Image Synthesis - Solve the Small Sample Size (SSS) problem
III. Reconstruction – Restore frontal illumination
Image Synthesis
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Erald Vuçini - Vienna University of Technology 13
Image Synthesis - Motivation
Face Recognition Systems
Performance related with training database
LDA require many samples per class
In many systems only one image per person is provided
Quotient Image makes possible the synthesis of the image space of a given input image
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Erald Vuçini - Vienna University of Technology 14
Lambertian Objects - Faces
The image space lives in a 3D linear subspace
Three images are sufficient for generating the image space of the object
TImage n s
Albedo Surface Normal
Light Source Direction
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Erald Vuçini - Vienna University of Technology 15
Quotient Image (Definitions)
Ideal class of facesSame shape
Different albedosT
i n s Synthesis Problem:
Given 3N images of N faces of the same class, illuminated under 3 lighting conditions
Synthesize image space of new input
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Erald Vuçini - Vienna University of Technology 16
Quotient Image (Definitions)
Given objects y and a we define quotient image Q by the ratio of their albedos
Q is illumination invariant
Image space of y can be generated with
Quotient Q
3 images of a
Generalization: Use bootstrap of 3N images
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Erald Vuçini - Vienna University of Technology 17
Quotient Image - Examples
Quotient
Quotient
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Erald Vuçini - Vienna University of Technology 18
Quotient Image – Image Space Synthesis
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Erald Vuçini - Vienna University of Technology 19
10 person Bootstrap
5 person Bootstrap
1 person Bootstrap
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Erald Vuçini - Vienna University of Technology 20
I. Dimensionality Reduction - LDA better than PCA regarding illumination
II. Image Synthesis - Solve the Small Sample Size (SSS) problem
III. Reconstruction – Restore frontal illumination
Reconstruction
![Page 21: Face Recognition Under Varying Illumination Erald VUÇINI Vienna University of Technology Muhittin GÖKMEN Istanbul Technical University Eduard GRÖLLER Vienna.](https://reader036.fdocuments.net/reader036/viewer/2022062407/56649d365503460f94a0db4c/html5/thumbnails/21.jpg)
Erald Vuçini - Vienna University of Technology 21
YaleB Testing Database
Yaleb Database
450 images of 10 persons
Divided in 4 subsets
Subset1 up to 10˚
Subset2 up to 25˚
Subset3 up to 45˚
Subset4 up to 75˚
Normalized
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Erald Vuçini - Vienna University of Technology 22
Histogram Equalization
Histogram equalization(HE) done as preprocessing increases the recognition rate
Adaptive HE(AHE) is used as a preprocessing step in the iterative face recognition approach
Results with the YaleB Database (PCA used)
No Preprocessing HE AHE
Recognition Rate(%) 43.4 74 81.5
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Erald Vuçini - Vienna University of Technology 23
Illumination Restoration Approach
A face image with arbitrary illumination is restored to having frontal illumination. It has the following advantages:
No need to estimate face surface normals
No need to estimate light source directions and albedos
No need to perform image warping
Face images will be visually natural looking
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Erald Vuçini - Vienna University of Technology 24
Algorithm Outline
Compute mean face image and eigenspace
Compute initial restored images
Create iteration by replacing Bro with blurred Hio
Continue iteration until stopping criteria satisfied
roio ik
ik
BH IB
Input Image Blurred Reference Image
Blurred Input Image
Restored Image
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Erald Vuçini - Vienna University of Technology 25
Iteration Steps
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Erald Vuçini - Vienna University of Technology 26
Experimental Results (Subset 3)
Restoration
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Erald Vuçini - Vienna University of Technology 27
Experimental Results (Subset 4)
Restoration
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Erald Vuçini - Vienna University of Technology 28
Results with YaleB Database
Subset1
Subset2
Subset3
Subset4
Overall
HE+PCA(%)
100 97.5 66.4 44.2 74
HE+New(%)
100 100 92.8 91.7 95.6
Subset1
Subset2
Subset3
Subset4
Overall
AHE+PCA(%)
100 100 83.57 50.0 81.6
AHE+New(%)
100 100 100 95.0 98.7
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Erald Vuçini - Vienna University of Technology 29
Thank you for the attention!
Proposed Method
Dimension Reduction
Image Synthesis
Reconstruction