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

1. 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) Lahoucine Ballihi (University Lille1/LIFL)

2. Outline • 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

3. 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

4. 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

5. Proposedapproach Reduced feature space Random Forest Adaboost SVM Training 3D scan PCA-based transformation Training stage Symmetry Capture (DSF) 3D scan preprocessing Testing stage Classification Testing 3D scan Female

6. SymmetryCapture • Represent facial surface S by a set of parameterized radial curves emanating from the nose tip. Nose tip Equal angular curves extraction On the face Preprocessed face Radial curves On the face

7. Symmetry Capture • Corresponding symmetrical curves , . • Capture symmetry by shape comparison of and .

8. 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. vs. Srivastava et al. TPAMI 11

9. GeodesicPaths on Sphere Geodesics in Rn are straight lines (Euclidean metric) Geodesic path connecting points p andq Derivative and module • Geodesicpath on Sphere

10. 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.

11. 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

12. Experiments • Evaluation protocol • FRGC-2.0 database (UND) • 466 earliest scans/4007 scans • 10-fold cross validation (person-independent)

13. 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.

14. Experiments (A) Robustness to age and ethnicity variations-466 scans Gender relates with face symmetry tightly Effectiveness & Robustness of approach • Comparable with different classifiers • Robust to number of Feature vectors • Achieve 90.99% with Random forest • Random Forest more effective

15. Experiments (A) Robustness to age and ethnicity variations-466 scans Observations: • Symmetricaldeformation on both sides • Low deformations near symmetry plane/ high deformations faraway • female deformation changes smoother than male

16. 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

17. Experiments (B) Robustness to expression variations-4007 scans Similar observations: • Symmetricaldeformation on both sides • Low deformations near symmetry plane/ high deformations faraway • female deformation changes smoother than male

18. Comparisonwith state-of-the-art

19. Comparisonwith 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.

20. 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.

21. 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) Gradient Spatial Symmetry

22. 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.

23. End