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

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1

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 1 2 2

1 2

1 2

, , , , ... ,

...

...

1 1 ... 1

TL Li i i i i i i

Li i i

Li i i i

x y x y x y

x x x

y y y

u

U

1

2 3

4

i ij jU H U

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2

Homographies

Homographies hold both for object or camera movements

The points must be coplanar

H

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3

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)

H

1H

Model Coordinates Image Coordinates

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4

Image Matching

ASM Image Model (Similarity) Gradient normal to the shape

contour Projective transformations

Do not preserve angles nor distance relationships

H

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5

AV@CAR Database (40 people)

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6

Results

PASM

ASM

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Training and test on multi-view data

Cross validation

7

Comparison to related work

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Ratios with respect to error on frontal images

8

Results training just a single view (frontal)

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Training set: Frontal Test set: Multilple views

9

Analysis of the single-view case

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10

Conclusions on PASM

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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

How reliable is the result?

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12