1. 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. 1

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 26/08/2015 2

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 26/08/2015 3

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 26/08/2015 4

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

6. Symmetry Capture 26/08/2015 6 Equal angular curves extraction On the face Preprocessed face Nose tip Radial curves On the face o Represent facial surface S by a set of parameterized radial curves emanating from the nose tip.

7. Symmetry Capture 26/08/2015 7 o Corresponding symmetrical curves,. o 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. 26/08/2015 8 Srivastava et al. TPAMI 11 vs.

9. Geodesic Paths on Sphere Geodesics in R n are straight lines (Euclidean metric) Geodesic path connecting points p and q Derivative and module 9 Geodesic path 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. 26/08/2015 10

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 26/08/2015 11

12. 12 26/08/2015 Evaluation protocol  FRGC-2.0 database (UND)  466 earliest scans/4007 scans  10-fold cross validation (person-independent) Experiments

13. 13 26/08/2015 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. 14 26/08/2015 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

15. 15 26/08/2015 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

16. 16 26/08/2015 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. 17 26/08/2015 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:

18. Comparison with state-of-the-art 26/08/2015 18

19. 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. 26/08/2015 19

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. 26/08/2015 20

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) 26/08/2015 21 Gradient SpatialSymmetry

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. 22

23. End 26/08/2015 23