Journee doctorant , December 12, 2012.
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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)
Outline
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 Introduction
 State-of-the-art
 Proposed 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
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 Motivation to this work

Why come to this idea ?
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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
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
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.

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cooperative
Based on small dataset
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Proposed approach
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Reduced
feature space
Random Forest
Adaboost
SVM
Training 3D scan
3D scan
preprocessing
Symmetry
Capture (DSF)
PCA-based
transformation
Training stage
Testing stage
Classification
Testing 3D scan
Female
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Symmetry Capture
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o
Represent facial surface S by a set of parameterized
radial curves emanating from the nose tip.
Nose tip
Preprocessed face
Equal angular curves extraction
On the face
Radial curves
On the face
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Symmetry Capture
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o
o
Corresponding symmetrical
curves
,
.
Capture symmetry by shape
comparison of
and
.
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Shape Analysis of Curves
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Represent each parameterized curve
on the face,
by Square-root velocity function q(t):

Elastic metric is reduced to the
Translations are removed

Isometry under rotation & re-parameterization.

metric.
vs.
Define the space of such functions defined as :
With Norm denoted by
on its tangent spaces,
becomes a Riemannian manifold.
Srivastava et al. TPAMI 11
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Geodesic Paths on Sphere
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Geodesics in Rn are straight
lines (Euclidean metric)
Geodesic path connecting
points p and q
Derivative and module
Geodesic path on Sphere
Dense Scalar Field (DSF)
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 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
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 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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Experiments
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 Evaluation protocol
 FRGC-2.0 database (UND)
 466 earliest scans/4007 scans
 10-fold cross validation (person-independent)
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Experiments
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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
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(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
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(A) Robustness to age and ethnicity variations-466 scans
Observations:
• Symmetrical deformation on both
sides
• Low deformations near symmetry
plane/ high deformations faraway
• female deformation changes
smoother than male
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Experiments
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(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
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(B) Robustness to expression variations-4007 scans
Similar observations:
• Symmetrical deformation on both
sides
• Low deformations near symmetry
plane/ high deformations faraway
• female deformation changes
smoother than male
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Comparison with state-of-the-art
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Comparison with state-of-the-art
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 General Comparison
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[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

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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
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 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,

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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
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 Deal with pose variation and incomplete data


Compute more descriptors
Fusion methods
Gradient
Spatial
Symmetry
 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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Publication
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
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.
End
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