1. Out-of-plane Rotations Environment constraints ● Surveillance systems ● Car driver images ASM: ● Similarity does not remove 3D pose ● Multiple-view database Other approaches ● Non-linear models ● 3D models: multiple views AV@CAR Database 1 2 3 1

2. Projective Geometry Geometric operations by means of linear algebra  2D points are 3- component vectors Multiple views of the same planar object can be related by homographies 1 2 3 4 1 2 3 2

3. Homographies Homographies hold both for object or camera movements The points must be coplanar H 1 2 3 3

4. Coplanar face model Silhouette points are excluded (out of main plane) Half the nose points are excluded (easy occlusion) First iteration: At least 8 correspondences to compute H (4 2D-points) Model Coordinates Image Coordinates 1 2 3 4

5. Image Matching ASM Image Model (Similarity) Gradient normal to the shape contour Projective transformations Do not preserve angles nor distance relationships H 1 2 3 5

6. AV@CAR Database (40 people) 1 2 3 6

7. Results PASM ASM 1 2 3 Training and test on multi-view data Cross validation 7

8. Comparison to related work 1 2 3 Ratios with respect to error on frontal images 8

9. Results training just a single view (frontal) 1 2 3 Training set: Frontal Test set: Multilple views 9

10. Analysis of the single-view case 1 2 3 10

11. Conclusions on PASM 1 2 3 If multi-view dataset available Almost invariant to rotations up to 60 degrees Training only on frontal views Considerably reduces (50%) variation of ASM due to viewpoint Left-right rotations better handled than up-down nodding Very difficult to compare to other results Points used for alignment can affect performance Not considerable for expected ASM precision 11

12. How reliable is the result? 1 2 3 4 12