3D Landmark Detection

1. 3D Landmark Detection Ricky Gentry August 2010

2. Problem • Given a textureless 3D face, we want to localize the prominent landmarks, regardless of its pose and expression.

3. Candidate Selection Methods • Shape Index • Gaussian Curvature • Spin Images

4. Shape Index and Curvature • k1 = maximum curvature • k2 = minimum curvature • Gaussian Curvature = k1 * k2 • Shape Index = ½ - 1/ π * atan((k1+k2)/(k1-k2)) • Shape index ranges from 0 to 1

5. Spin Images • Plane is swept around a point along its normal. • Plane captures vertices. • Created from ground truth of data set. Inner Eye Corner Nose Tip

6. Algorithm • Select candidates using properties. • Create sets of rigid landmarks and select set of best combinations • Outer eye corners • Inner eye corners • Nose tip • Nasal alae • Upper Lip • Create sets of nonrigid landmarks and select set of best combinations • Mouth Corners • Lower Lip • Chin Tip • Merge sets of rigid and nonrigid landmarks to create half faces • Merge half face sets to find full face. • Select best of candidate FLMs.

7. Candidate Selection • Nose tip and chin tip have high shape index and high similarity to the produced spin image. • Nasal alae have negative gaussian curvature • Lips are extruded. • Eye have low shape index and are intruded.

8. Results