gender and 3d facial symmetry: what ’ s the relationship ?
DESCRIPTION
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 PresentationTRANSCRIPT
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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
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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.
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Symmetry Capture
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o Corresponding symmetrical curves , .
o Capture symmetry by shape comparison of and .
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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.
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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
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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:
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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
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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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