Progress Report

February 22nd, 2005

Overview

• Face models
  • Facial appearance models
  • Evaluation and perturbation
  • Results
• Model Evaluation
  • Number of modes (in brain)
  • Future paths
• Summary

Masked Faces

• The background is omitted from all training images

• The training set becomes easier to compare

Faces: Models Sigma

• Models can then be built from a set

Faces: Models Sigma - Ctd.

• Perturbation of the landmarks affects model quality

Perturbation with Sigma=3

Masked Faces - Synthesis

• Some of the synthetic images from the model:

• Images still need to be re-scaled to overlap

Face Model Evaluation

• Experiments investigate landmark noise
• Sigma ranging from 0 to 5 inclusive
• Distance measures based on 1000 syntheses
• Specificity was expected to rise as a function of sigma

Face Model Evaluation: Results

• Generalisability

Face Model Evaluation: Results

• Specificity

Face Model Evaluation: Results

• Example distance matrix

Brain Model Evaluation: Explanation

• We investigate the effect of the number of modes
• Modes which are used to evaluate a model
• Synthetic data is generated from the appearance model
• A finite number of principal modes is selected

Brain Model Evaluation: Explanation

• Investigate the effect of the number of modes
• Referring to modes which are used to evaluate a model
• Synthetic data is generated from the appearance model
• A finite number of principal modes is selected

Brain Model Evaluation: Figures

• If all modes are altered in generation simultaneously...
• ...distance measures become less meaningful
• Hence more synthetic data is required

Brain Model Evaluation: Experiments

• 3 models were used to learn the effect of the number of modes:
  • Model I: built from the results of pair-wise registration
  • Model II: built using the initialisation algorithm
  • Model III: built using group-wise registration

Brain Model Evaluation: Results

• Generalisability

Brain Model Evaluation: Results

• Specificity

Brain Model Evaluation: Conclusions

• Generalisability on a steady decrease
• Group-wise outperforms pair-wise regardless of number of modes
• Complex methods surpass the basic initialisation algorithm
• Specificity of group-wise almost independent of number of modes
• Specificity of pair-wise degrades approximately linearly

Brain Model Evaluation: Future

• Normalisation in evaluation:
  • Set size
  • Other factors
• Extension of training set using synthetic data
• Larger experiments

MICCAI / BMVC

• Deadline is mid-April for both
• Wiki:
  • A Wiki can be set up
  • Used for collaborated editing of content
  • Web-based and database driven
  • Easy rollback, backup, 'diff'

Possible Paper Structure [1]

Introduction

• Pair-wise versus group-wise
• Automatic model-building
• Model evaluation
• Proof of group-wise being most desirable

Possible Paper Structure [2]

Automatic Model-Building

• Explanation of the algorithm/s
• Hopefully tetrahedronisation
• If 3-D results are available:
  • Pick a slice from the volume to evaluate the 3-D model
  • This will provide an approximation
• Initialisation algorithm in 3-D

Possible Paper Structure [3]

Model Evaluation

• Explanation of the methods:
  • Shuffle distance
  • Normalisation, scaling issues
  • Deriving Spec. and Gen. in relation to work on shape

Possible Paper Structure [4]

Brain

• Sigma of noise versus Spec. and Gen.
• Spec. and Gen. versus the number of modes
• Spec. (maybe also Gen.) for different registration methods

Possible Paper Structure [5]

Faces

• Show faces from the model
• Show noise being applied to face mark-up and the resulting model/s
• Experiments with noise - an explanation
• Specificity and Generalisability versus sigma

Possible Paper Structure [6]

Conclusions, Summary, Discussion, etc.

• Repeating the main points from the sections above
• Future work:
  • Full 3-D (if not already done)
  • Larger training set (possibility of creating pseudo-training data)
  • Further investigation of registration methods
  • Investigation of evaluation parameters