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Journee doctorant , December 12, 2012. 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) - PowerPoint PPT Presentation
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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)
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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