CONTENT BASED FACE RECOGNITION

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CONTENT BASED FACE RECOGNITION
Under the guidance of Prof. Pushpak
Bhattacharya

• Ankur Jain 01D05007
• Pranshu Sharma 01005026
• Prashant Baronia 01D05005
• Swapnil Zarekar 01D05001

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Introduction

• Problem Statement
• Given an image, to identify it as a face and/or
  extract face images from it.
• To retrieve the similar images (based on a
  heuristic) from the given database of face
  images.

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Why face recognition ?

• Various potential applications, such as
• person identification.
• human-computer interaction.
• security systems.

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Difference From Image Recognition

• Faces are complex, multidimensional
  and meaningful visual
  stimuli.
• Face Recognition is difficult.
• Face Images are similar in overall configuration.

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Approach

• Similar to Content Based Image Retrieval (CBIR).
• Neural Networks and Self Organizing Maps (SOMs).
• Principal Component Analysis (PCA).
• Relevance feed back.

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• Stages of Face Recognition
• (1) face location detection
• (2) feature extraction
• (3) facial image classification
• Approaches of Feature Extraction
• (1) local feature eyes, nose, mouth
  information
• easily affected by irrelevant
  information .
• (2) global feature
• extract feature from whole image .

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Face Recognition Using Eigenfaces
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Eigen Space and Eigen Faces

• Face Images are projected into a feature space
  (Face Space) that best encodes the variation
  among known face images.
• The face space is defined by the eigenfaces,
  which are the eigenvectors of the set of faces.

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Steps In Face Recognition

• Initialization
• Acquire the training set and calculate eigenfaces
  (using PCA projections) which define eigenspace.
• When a new face is encountered, calculate its
  weight.
• Determine if the image is face.
• If yes, classify the weight pattern as known or
  unknown.
• (Learning) If the same unknown face is seen
  several times incorporate it into known faces.

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PCA

• Main assumption of PCA approach
• Face space forms a cluster in image space.
• PCA gives suitable representation.

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Eigenfaces (1)

• Calculation of Eigenfaces
• (1) Calculate average face v.
• (2) Collect difference between training images
  and average face in matrix A (M by N), where M is
  the number of pixels and N is the number of
  images.
• (3) The eigenvectors of covariance matrix C (M
  by M) give the eigenfaces.
• M is usually big, so this process would be time
  consuming.
• What to do?

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Eigenfaces (2)

• Calculation of Eigenvectors of C
• If the number of data points is smaller than the
  dimension (Nmeaningful eigenvectors.
• Instead of directly calculating the eigenvectors
  of C, we can calculate the eigenvalues and the
  corresponding eigenvectors of a much smaller
  matrix L (N by N).
• if ?i are the eigenvectors of L then A ?i are
  the eigenvectors for C.
• The eigenvectors are in the descent order of the
  corresponding eigenvalues.

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Eigenfaces (3)

• Representation of Face Images using Eigenfaces
• The training face images and new face images can
  be represented as linear combination of the
  eigenfaces.
• When we have a face image u
• Since the eigenvectors are orthogonal

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Eigenfaces (4)

• Experiment and Results
• Data used here are from the ORL database of
  faces. Facial images of 16 persons each with 10
  views are used. - Training set contains 167
  images.
• - Test set contains 163 images.
• First three eigenfaces

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Classification Using Nearest Neighbor

• Save average coefficients for each person.
  Classify new face as the person with the closest
  average.
• Recognition accuracy increases with number of
  eigenfaces till 15.
• Later eigenfaces do not help much with
  recognition.
• Best recognition rates
• Training set 99
• 
  Test set 89

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Neural Networks and TS-SOM
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What are Neural Networks ?

• Individual units to simulate Neurons
• Parallel Processing
• Many inputs and single output
• Organization/structure of the TLUs is important

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What is SOM ?

• TS-SOM - Tree structure self-organizing maps
• Competitive learning ANN
• Each unit of map receives identical inputs
• Units compete for selection
• Modification of selected node and its neighbors

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Training of SOM

• Randomly initialized
• Selection based on some query parameter
• On selection a node and its neighbors are
  modified
• Degree of modification reduces with each iteration

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Example of a two-dimensional TS-SOM structure of
3 levels
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Algorithm

• Calculate weight vector for first level.
• Initialize weight vectors of other levels.
• Calculate centroid associated to each node as
  mean of closest training samples.
• Iterate to the next level.

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Relevance Feedback

• System content based retrieval.
• Point of human intervention
• User analysis of system output.
• User selects most relevant
• Query iterated if output not satisfactory

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Interaction Between User System

• A random set of faces is presented to the user.
• User interactive selection of faces.
• System content-based face retrieval.
• User analysis of retrieved faces.
• Requested face was found - Exit
• Similar faces were found. - Go
  to 2
• No similar faces were found.
• User tired
  - Exit
• User not tired (re initialization - Go
  to 1

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Comparison of the Two Approaches

• Training time
• Nearest neighbor is much faster.
• Storage
• About the same.
• Classification time
• Nearest neighbor is slightly slower.
• Accuracy
• Neural network is able to achieve the same
  accuracy using 5 eigenfaces with nearest neighbor
  using 15, and a higher accuracy when using 15.
• Neural network models the problem
  better, but takes more training time.

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Future Work

• Face Detection in motion pictures.
• Detailed study of the proposed system assuming
  PCA assumptions not to be true.
• Investigate whether eigenfaces is a good solution
  for this problem by comparing with other feature
  extraction techniques such as DCT

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References

• Navarrete P. and Ruiz-del-Solar J. (2002),
  Interactive Face Retrieval using Self-Organizing
  Maps, 2002 Int. Joint Conf. on Neural Networks
  IJCNN 2002, May 12-17, Honolulu, USA.
• A tutorial on Principal Components Analysis, By
  Lindsay I Smith.
• Eigenfaces for Recognition, Turk, M. and
  Pentland A., (1991)Journal of Cognitive
  Neuroscience, Vol. 3, No. 1, pp. 71-86.
• Ruiz-del-Solar, J., and Navarrete, P. (2002).
  Towards a Generalized Eigenspace-based Face
  Recognition Framework, 4th Int. Workshop on
  Statistical Techniques in Pattern Recognition,
  August 6-9, Windsor, Canada.
• Simulating Neural Networks by James A. Freeman.
• Artificial Intelligence by Neil J. Nielsson.