Face Recognition and Detection Using Eigenfaces

1
Face Recognition and Detection Using
Eigenfaces Based on M.A. Turk and A. P.
Pentland,Face Recognition Using
Eigenfaces,Proc. IEEE Conf. on CVPR, Maui, HI,
USA, pp. 586-591, Jun. 1991. Kohsia Huang ECE
285 Class Presentation Prof. Mohan
Trivedi Winter, 2001 Department of Electrical
and Computer Engineering University of
California, San Diego
2
Background Works

• Detecting individual face features
• - Difficult to extend to non-frontal views
• - Insufficient representation for face
  identification
• Neural network approaches
• Multiresolution template matching
• Other methods
• - A. Pentland and T. Choudhury, Face
  Recognition for Smart Environments, IEEE Comp.
  Mag., pp. 50-55, Feb. 2000.
• - P. Penev and J. Atick, Local Feature
  Analysis A General Statistical Theory for Object
  Representation, Network Compu. in Neural Syst.
  7, pp. 477-500, Mar. 1996.

3
Interpretations of Eigenface

• Information Theory Extract relevant information
  in face images, encode face images efficiently,
  and compare individual face images.
• Linear Algebra Find principle components of the
  distribution of faces, which is the eigenvectors
  of the covariance matrix of the training faces.
  Principle components Features Eigenfaces.

4
Eigenface Algorithm

• Dimension reduction Face images can be
  represented as a linear combination of the
  eigenfaces.
• Approximation The feature space or eigenface
  space can be approximated by the eigenfaces
  associated with the largest eigenvalues.

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Eigenface Example
6
Procedure

• Initialization Obtain training faces and
  calculate the eigenfaces.
• Operating Calculate a set of weights by
  projecting the test face into eigenface space.
• Face detection If the image is close to the face
  space, it is a face image.
• Recognition If the test face is close to a
  certain training face, it is recognized.

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Formulation
8
Face Detection

• The error is the difference between the original
  image and its projection image onto eigenface
  space.
• If the error is within a threshold, the image is
  detected as a face image.
• Efficient calculation available.

9
Face Recognition

• If the projection of face image onto eigenface
  space is close to one training face, it is
  identified as that training face.
• Distance measure can be Euclidian distance.

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Classification Summary

• Four possible patterns of an input image
• Near face space and its projection is near a face
  class ? Recognized.
• Near face space but its projection is distant
  from all face classes ? Unknown face.
• Distant from face space but its projection is
  near a face class ? Not a face image.
• Distant from face space and its projection is
  distant from all face classes ? Not a face image.
  

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Implementation
12
Accuracy

• 2500 face images
• Infinite thresholds 96 correct on lighting
  variation, 85 on face orientation variation, 64
  on size (zooming) variation.
• Limited thresholds Adjust unknown rate to 20,
  the above correct rates becomes 100, 94, and
  74, respectively.
• 25 face images
• 74 correct rate for controlled conditions.

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Accuracy (Cont.)

• FERET Competition
• Standardized testing criteria.
• Not accurate enough for lower dimension
  eigenface spaces.
• Needs approximately 120 dimensions to compete
  with local feature analysis.