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,

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

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

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 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

Symmetry Capture 26/08/ 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.

Symmetry Capture 26/08/ 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. 26/08/ Srivastava et al. TPAMI 11 vs.

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

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/

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/

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

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 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 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 26/08/2015 Experiments (B) Robustness to expression variations 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 26/08/2015 Experiments (B) Robustness to expression variations 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 26/08/

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/

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/

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/ Gradient SpatialSymmetry

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,

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