MINORI project (http://www.eastman.ucl.ac.uk/~dmi/MINORI),

Biomedical Informatics Unit (http://www.eastman.ucl.ac.uk/~dmi),

Eastman Dental Institute (http://www.eastman.ucl.ac.uk),

UCL.

Example: marcusexample.jpg

Method: standard Cootes et al. PDMs (Procrustes + PCA)

Mode 1 (48.0%): marcus2dm1.gif (between -3 and +3 standard deviations, all other modes kept at zero)

Mode 2 (29.1%): marcus2dm2.gif (between -3 and +3 standard deviations, all other modes kept at zero)

Example: cephexample.jpg

Method: standard Cootes et al. PDMs (Procrustes + PCA)

Mode 1 (37.0%): zmode1.avi (between -3 and +3 standard deviations, all other modes kept at zero)

Mode 2 (18.7%): zmode2.avi (between -3 and +3 standard deviations, all other modes kept at zero)

Method: standard Cootes et al. method of piecewise affine warping images to mean landmarks then applying PCA to intensities

Mode 1 (?%): sfm1.gif (between -3 and +3 standard deviations, all other modes kept at zero)

Method: combining PCA of modes of shape and intensity, made commensurate by maximising entropy [explain this] (alternate is ratio of eigensums [explain this]) Old method of Cootes et al. (moving landmarks and finding weighting by regression is a little perverse (and doesn't extend to 3D).

Mode 1 (?%): cam2dm1.gif (between -3 and +3 standard deviations, all other modes kept at zero)

Example: rick.gif (animation of rotation to show 3D nature)

Data: ~30 face surfaces landmarked with ~20 landmarks by TJH

Method: TJH's old method (Procrustes (scale taken out) + PCA on 3D
landmarks, TPS-warping 3D surfaces to the mean landmarks, averaging of
vertices to form mean mesh, combining PCA of 3D landmark modes and previous
2D grey-level model modes using ratio of eigensums - generate examples
by TPS-warping mean mesh to desired 3D landmarks and texture-mapping the
synthesised image)

Mode 1 (?%): cam3dm1.gif (between -3 and
+3 standard deviations, all other modes kept at zero)

Mode 2 (?%): cam3dm2.gif (between -3 and
+3 standard deviations, all other modes kept at zero)

Data: 17 scans of a single person (Marcus) in a variety of facial expressions
landmarked with 71 landmarks by TJH

Method: TJH's Dense Surface Model (Procrustes (scale left in) on 3D
landmarks, TPS-warping 3d surfaces to the mean landmarks, dense correspondence
using nearest point search from a base mesh, inverse TPS warping resampled
meshes back to original places, Procrustes on meshes, PCA on meshes, combining
PCA of these modes and previous 2D grey-level model modes using ratio of
eigensums)

Mode 1 (29.8%): marcusm1.gif (between -3
and +3 standard deviations, all other modes kept at zero)

Mode 2 (18.4%): marcusm2.gif (between -3
and +3 standard deviations, all other modes kept at zero)

Mode 3 (13.2%): marcusm3.gif (between -3
and +3 standard deviations, all other modes kept at zero)

Data: 31 images of a single person (Siben) in an amusing variety of
facial expressions landmarked with 21 landmarks by TJH

Method: TJH's Dense Surface Model (DSM)

Mode 1 (?%): sibenmode1.gif (between -3
and +3 standard deviations, all other modes kept at zero)

Mode 2 (?%): sibenmode2.gif (between -3
and +3 standard deviations, all other modes kept at zero)

Mixed mode: sibenmixedmode.gif (mode1=-2.0,
mode2=2.0, mode3 between -3 and 3, all others zero)

Method: Fit initialized with average mesh in approximate position, combination of ICP and ASM search (using DSM) to translate, scale, rotate and deform mesh to target mesh.

fit1.mpg: in-training-set search to image of Marcus with his mouth open (target in semitransparent grey, model in green)

fit4.mpg: fit to unseen example (other person) (target in semitransparent grey, model in green)

fit5.mpg: in-training-set search to image of Marcus mid-blink (target in semitransparent grey, model textured with average image to better show fitting - eyes close to match target)

Method: DSM gives scatter in n-dimensions, kernel-smoothing on age is used to find average 1 yr old, 2 yr old, 3 yr old., etc. (kernel is triangular, width = 20yrs - hence extremes are pulled-in)

aging.avi: note large shape change in youth then very little later in life, but more subtle skin tone changes with age, plus nose grows throughout life.

ageline.jpg: screen capture of 3D scatter plot (examples coloured by age (blue=young, red=old) on the first three modes, mode 1 stretched for clarity) with estimate of age trajectory in green.