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Bi model face recognition framework

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Sumit Agarwal
Sumit Agarwal
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Bi-model face recognition framework [For expression invariant face recognition]
Expressions: Two perspectives.  1) Noise?
Expressions  11) Extra features ?  Essentially everyone has got a different smile here.
Outline  Principal Component Analysis  Reduce dimensionality.  Extract low dimensional features from original facial appearance.  Fisher Linear Discriminant Analysis  To maximize the discrimination between different classes.  Fusion of Modalities.  Well we have not done much of work on this front yet.
Principal Component Analysis [PCA]  What is it?  A way to identify patterns in the data.  Reduce or compress the data without loss of much information.  In our case: introduce generalization. What happens when we increase number of neurons?
PCA : Walkthrough - 1  Get some data  Subtract the mean [Data Adjust].
PCA : Walkthrough - 2 Data plotted on x-y
PCA : Walkthrough - 3  Calculate the covariance matrix  Calculate the eigenvalues and eigenvectors  The eigenvector with highest eigenvalue is Principal Component.
PCA : Walkthrough - 4
PCA : Walkthrough - 5
Fisher Linear Discriminant Analysis  PCA finds the most accurate data representation for lower dimensional space.  But directions of maximum variance may be useless for classification. #Fail
FLDA – Main idea  Find projection to a line, or plane in case of higher dimension such that different classes are well separated.
FLDA - 1  Find vector such that the distance between the projection of classes is maximum along that vector.  But how to define distance?  Good along vertical axis, not so good along horizontal axis
FLDA - 2  The problem is that we did not consider *scatter* of the data.  Ok, wait!! What is scatter?  Scatter is variance multiplied by no. of elements.
FLDA - 3  Fisher solution : Normalize by scatter.  Thus Fisher linear discriminant is to project on line in the direction of v which maximizes  J(v) is the distance between mean values of two classes which are normalized by scatter.  After performing few complicated looking simple vector algebraic steps we get
FLDA - Example PCA FLDA
Multiple Modalities  Background  Rest of the story during final presentation and demo.
Questions?
Thank You

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Bi model face recognition framework

  • 1. Bi-model face recognition framework [For expression invariant face recognition]
  • 3. Expressions  11) Extra features ?  Essentially everyone has got a different smile here.
  • 4. Outline  Principal Component Analysis  Reduce dimensionality.  Extract low dimensional features from original facial appearance.  Fisher Linear Discriminant Analysis  To maximize the discrimination between different classes.  Fusion of Modalities.  Well we have not done much of work on this front yet.
  • 5. Principal Component Analysis [PCA]  What is it?  A way to identify patterns in the data.  Reduce or compress the data without loss of much information.  In our case: introduce generalization. What happens when we increase number of neurons?
  • 6. PCA : Walkthrough - 1  Get some data  Subtract the mean [Data Adjust].
  • 7. PCA : Walkthrough - 2 Data plotted on x-y
  • 8. PCA : Walkthrough - 3  Calculate the covariance matrix  Calculate the eigenvalues and eigenvectors  The eigenvector with highest eigenvalue is Principal Component.
  • 11. Fisher Linear Discriminant Analysis  PCA finds the most accurate data representation for lower dimensional space.  But directions of maximum variance may be useless for classification. #Fail
  • 12. FLDA – Main idea  Find projection to a line, or plane in case of higher dimension such that different classes are well separated.
  • 13. FLDA - 1  Find vector such that the distance between the projection of classes is maximum along that vector.  But how to define distance?  Good along vertical axis, not so good along horizontal axis
  • 14. FLDA - 2  The problem is that we did not consider *scatter* of the data.  Ok, wait!! What is scatter?  Scatter is variance multiplied by no. of elements.
  • 15. FLDA - 3  Fisher solution : Normalize by scatter.  Thus Fisher linear discriminant is to project on line in the direction of v which maximizes  J(v) is the distance between mean values of two classes which are normalized by scatter.  After performing few complicated looking simple vector algebraic steps we get
  • 17. Multiple Modalities  Background  Rest of the story during final presentation and demo.