Gender and 3D Facial Symmetry: What’s the Relationship ?

Xia BAIQIANG (University Lille1/LIFL)Boulbaba Ben Amor (TELECOM Lille1/LIFL)

Hassen Drira (TELECOM Lille1/LIFL)Mohamed Daoudi (TELECOM Lille1/LIFL)Lahoucine Ballihi (University Lille1/LIFL)

Journee doctorant , December 12, 2012.

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Outline

IntroductionState-of-the-artProposed approach

Methodology Symmetry Capture Dense Scalar Field (DSF) Gender Classification

Experiments Robustness to age and gender variations Robustness to expression variations

Conclusions and future directions

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Introduction

Motivation to this work Why come to this idea ?

Gender is essential visual attribute in human face Human faces are approximately symmetric

Why use 3D face, not 2D face ? Robust to illumination and pose changes Capture more details face information

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State-of-the-art

Liu et al. used Variance Ratio (Vr) of symmetric height and orientation differences in face regions for gender classification. 111 full 3D face models were used and a result of 96.22% was achieved with a linear classifier. cooperative Based on small dataset

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Training stage3D scanpreprocessi

ng Testing stage

SymmetryCapture (DSF)

Random ForestAdaboostSVM

PCA-based transformati

on

Female

Reducedfeature space

Classification

Training 3D scan

Testing 3D scan

Proposed approach

Symmetry Capture

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Equal angular curves extractionOn the face

Preprocessed face

Nose tip

Radial curvesOn the face

o Represent facial surface S by a set of parameterized radial curves emanating from the nose tip.

Symmetry Capture

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o Corresponding symmetrical curves , .

o Capture symmetry by shape comparison of and .

Shape Analysis of Curves

Represent each parameterized curve on the face,

by Square-root velocity function q(t): Elastic metric is reduced to the metric. Translations are removed

Isometry under rotation & re-parameterization.

Define the space of such functions defined as :

With Norm denoted by on its tangent spaces, becomes a Riemannian manifold.

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Srivastava et al. TPAMI 11

vs.

Geodesic Paths on Sphere

Geodesics in Rn are straight lines (Euclidean metric)

Geodesic path connecting points p and q

Derivative and module

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Geodesic path on Sphere

Dense Scalar Field (DSF)

For curve and its symmetrical curve , considering the module of at each point, , located in curve with index k. With all and K considered, we build a Dense Scalar Field (DSF) for each face.

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Gender classification

High dimensional feature space 200 curves/face 100 points/curve

PCA-based dimensionality reduction for SVFs Reduced subspace

Machine learning Algorithm Random Forest Adaboost SVM

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Evaluation protocol FRGC-2.0 database (UND)

466 earliest scans/4007 scans 10-fold cross validation (person-independent)

Experiments

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Experiments

FRGC-2.0 database (UND)

--Gender: 1848/203 females, 2159/265 males

--Age : 18 to 70 (92.5% in 18-30)

--Ethnicity : White 2554/319 Asian 1121/99 Other 332/48

--Expression : ~60% scans neutral

--Pose : All scans in FRGC-2.0 are near-frontal.

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Experiments

(A) Robustness to age and ethnicity variations-466 scans

◦ Comparable with different classifiers

◦ Robust to number of Feature vectors

◦ Achieve 90.99% with Random forest

◦ Random Forest more effective

Gender relates with face symmetry tightly

Effectiveness & Robustness of approach

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Experiments

• Symmetrical deformation on both sides

• Low deformations near symmetry plane/ high deformations faraway

• female deformation changes smoother than male

Observations:

(A) Robustness to age and ethnicity variations-466 scans

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Experiments

(B) Robustness to expression variations-4007 scans

◦ Robust to number of Feature vectors ◦ Achieve 88.12% with Random forest

Gender relates with face symmetry tightly

Effectiveness & Robustness of Our approach

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Experiments

(B) Robustness to expression variations-4007 scans

• Symmetrical deformation on both sides

• Low deformations near symmetry plane/ high deformations faraway

• female deformation changes smoother than male

Similar observations:

Comparison with state-of-the-art

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Comparison with state-of-the-art

General Comparison [8], [7] , [5] based on small Dataset [8], [7], [6], [5] require manual landmarking [9], [8], [7], [5] not 10-fold cross-validation

Comparison with Nearest works Work1 achieves higher result than [20] with 466 scans Work2 uses whole FRGC-2.0 other than 3676 scans in [15]

Weak point Dependence to upright-frontal scans.

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Summary and conclusions

Propose a fully-automatic bilateral symmetry-based 3D face gender classification approach using DSF, which is also robust to age, ethnicity and expression variations.

Achieve comparable results with state-of-art, 90.99% ± 5.99 for 466 earliest scans 88.12% ± 5.53 on whole FRGC-2.0.

Demonstrate that significant relationship exists between gender and 3D facial Asymmetry.

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Future directions

Deal with pose variation and incomplete data Compute more descriptors Fusion methods

Combining texture and shape, and 2D/3D methods collaboration with Chinese partners.

Using symmetry-based approach for other related areas . (Age estimation result : 74% , 466 scans)

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Gradient Spatial Symmetry

Publication

Xia BAIQIANG ,Boulbaba Ben Amor ,Hassen,Mohamed Daoudi ,Lahoucine Ballihi, “Gender and 3D Facial Symmetry What’s the Relationship?” ,The 10th IEEE Conference on Automatic Face and Gesture Recognition, 2013.

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End

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