Automatic face recognition under component based manifolds
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Automatic Face Recognition under Component-Based Manifolds. CVGIP 2006 Wen-Sheng Chu ( 朱文生 ) and Jenn-Jier James Lien ( 連震杰 ) Robotics Lab. CSIE NCKU. Motivation. Face recognition is hard due to several image variations :. expression. pose. illumination. Person A. Person A. Objective.

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Automatic Face Recognition under Component-Based Manifolds

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Automatic face recognition under component based manifolds

Automatic Face Recognition under Component-Based Manifolds

CVGIP 2006

Wen-Sheng Chu (朱文生) and Jenn-Jier James Lien (連震杰)

Robotics Lab. CSIE NCKU


Motivation

Motivation

  • Face recognition is hard due to several image variations:

expression

pose

illumination


Objective

Person A

Person A

Objective

  • Recognize faces using multiple face patterns rather than a single one.

Person B

Person B

Single input pattern

Multiple input patterns


Automatic acquisition of facial components

Feature Point Detection

Automatic Acquisition of Facial Components

Training Data of Features

Rejected Non-face

2-Class SVM Classifiers

Detected Features

Original Image

Cropped Face I

Face

Detection

Face +ve

Removal

Facial Components Extraction

Registration byAffine Warping

Band-pass

Filtering

Normalized

Pose IR

Normalized

Illumination IB

Extracted Facial Components

P. Viola and M. Jones, “Robust Real-Time Face Detection”, IJCV 2004.


Automatic acquisition of facial components1

Feature Point Detection

Automatic Acquisition of Facial Components

Training Data of Features

Rejected Non-face

2-Class SVM Classifiers

Detected Features

Original Image

Cropped Face I

Face

Detection

Face +ve

Removal

Facial Components Extraction

Registration byAffine Warping

Band-pass

Filtering

Normalized

Pose IR

Normalized

Illumination IB

Extracted Facial Components


Facial feature detector

x

x

x

Facial Feature Detector

  • 2-class SVM with feature vector v:

  • Reject false positives

o

o

o

o


Automatic acquisition of facial components2

Feature Point Detection

Automatic Acquisition of Facial Components

Training Data of Features

Rejected Non-face

2-Class SVM Classifiers

Detected Features

Original Image

Cropped Face I

Face

Detection

Face +ve

Removal

Facial Components Extraction

Registration byAffine Warping

Band-pass

Filtering

Normalized

Pose IR

Normalized

Illumination IB

Extracted Facial Components


Registration illumination normalization

Registration & Illumination Normalization

Registration

Affine warping

Band-pass filtering

Illumination

Normalization


Automatic acquisition of facial components3

Feature Point Detection

Automatic Acquisition of Facial Components

Training Data of Features

Rejected Non-face

2-Class SVM Classifiers

Detected Features

Original Image

Cropped Face I

Face

Detection

Face +ve

Removal

Facial Components Extraction

Registration byAffine Warping

Band-pass

Filtering

Normalized

Pose IR

Normalized

Illumination IB

Extracted Facial Components


Facial components extraction

Facial Components Extraction

  • Effects of pose and illumination are smaller in each local region compared with those in the holistic face image.

T. K. Kim, H. Kim, W. Hwang and J. Kittler, “Independent Component Analysis in A Local Facial Residue Space for Face Recognition”, PR, 2004.


Constrained mutual subspace method cmsm

Constrained Mutual Subspace Method (CMSM)

  • Similarity between i and j == θc

  • Use the variation of dissimilarity between subjects

subspace j

subspace i

θ

project

project

constrainedsubspace

θc

ic

jc

K. Fukui and O. Yamaguchi, “Face Recognition Using Multi-viewpoint Patterns for Robot Vision”, ISRR 2003.


Constrained subspace generation

PCA basis

Constrained Subspace Generation

  • Take nose for explanation:

    The eigenvectors, w, selected in ascending order, are the basis of the constrained subspace, Snose.

Constraint subspace basis


Projection onto constrained subspace

Projection onto Constrained Subspace

  • Projection

    basis vectors  constrained subspace Snose

  • Normalization

    length(projected vector)  1

  • Orthogonalization

    applying Gram-Schmidt process to orthogonalize the normalized vectors

Snose


Comparison between normalized manifolds

Comparison between Normalized Manifolds

  • The similarity of nose between subject i and subject j:

    where are defined as the eigenvalues of matrix .

  • Similarity(i, j) == summing up the five canonical correlations


Experiment setup

Experiment Setup


Typical samples in 3d principal component space holistic image

Typical Samples in 3D Principal Component Space – Holistic Image

subject 1 (․)

subject 2 (․)

subject 3 (․)

subject 4 (․)


Original v s projected subspaces eye braw

Original v.s. Projected Subspaces – Eye-braw


Original v s projected subspaces left eye

Original v.s. Projected Subspaces – Left Eye


Original v s projected subspaces right eye

Original v.s. Projected Subspaces – Right Eye


Original v s projected subspaces nose

Original v.s. Projected Subspaces – Nose


Original v s projected subspaces mouth

Original v.s. Projected Subspaces – Mouth


Comparison

Comparison


Automatic face recognition under component based manifolds

End

F&Q and thanks!


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