Face Image Analysis by Unsupervised Learning

Marian Stewart Bartlett, Kluwer Academic Publishers, 2001

Contents

  1. SUMMARY


  2. INTRODUCTION
    1. Unsupervised learning in object representations
      1. Generative models
      2. Redundancy reduction as an organizational principle
      3. Information theory
      4. Redundancy reduction in the visual system
      5. Principal component analysis
      6. Hebbian learning
      7. Explicit discovery of statistical dependencies
    2. Independent component analysis
      1. Decorrelation versus independence
      2. Information maximization learning rule
      3. Relation of sparse coding to independence
    3. Unsupervised learning in visual development
      1. Learning input dependencies: Biological evidence
      2. Models of receptive field development based on correlation sensitive learning mechanisms
    4. Learning invariances from temporal dependencies in the input
      1. Computational models
      2. Temporal association in psychophysics and biology
    5. Computational algorithms for recognizing faces in images


  3. INDEPENDENT COMPONENT REPRESENTATIONS FOR FACE RECOGNITION
    1. Introduction
      1. Independent component analysis (ICA)
      2. Image data
    2. Statistically independent basis images
      1. Image representation: Architecture 1
      2. Implementation: Architecture 1
      3. Results: Architecture 1
    3. A factorial face code
      1. Independence in face space versuspixel space
      2. Image representation: Architecture 2
      3. Implementation: Architecture 2
      4. Results: Architecture 2
    4. Examination of the ICA Representations
      1. Mutual information
      2. Sparseness
    5. Combined ICA recognition system
    6. Discussion


  4. AUTOMATED FACIAL EXPRESSION ANALYSIS
    1. Review of other systems
      1. Motion-based approaches
      2. Feature-based approaches
      3. Model-based techniques
      4. Holistic analysis
    2. What is needed
    3. The Facial Action Coding System (FACS)
    4. Detection of deceit
    5. Overview of approach


  5. IMAGE REPRESENTATIONS FOR FACIAL EXPRESSION ANALYSIS: COMPARITIVE STUDY I
    1. Image database
    2. Image analysis methods
      1. Holistic spatial analysis
      2. Feature measurement
      3. Optic flow
      4. Human subjects
    3. Results
      1. Hybrid system
      2. Error analysis
    4. Discussion


  6. IMAGE REPRESENTATIONS FOR FACIAL EXPRESSION ANALYSIS: COMPARITIVE STUDY II
    1. Introduction
    2. Image database
    3. Optic flow analysis
      1. Local velocity extraction
      2. Local smoothing
      3. Classification procedure
    4. Holistic analysis
      1. Principal component analysis: ``EigenActions''
      2. Local feature analysis (LFA)
      3. ``FisherActions''
      4. Independent component analysis
    5. Local representations
      1. Local PCA
      2. Gabor wavelet representation
      3. PCA jets
    6. Human subjects
    7. Discussion
    8. Conclusions


  7. LEARNING VIEWPOINT INVARIANT REPRESENTATIONS OF FACES
    1. Introduction
    2. Simulation
      1. Model architecture
      2. Competitive Hebbian learning of temporal relations
      3. Temporal association in an attractor network
      4. Simulation results
    3. Discussion


  8. CONCLUSIONS AND FUTURE DIRECTIONS

References
Index