Face Recognition with Harr Transforms and SVMs

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Face Recognition with Harr Transforms and SVMs

• EE645 Final Project
• May 11, 2005
• J Stautzenberger

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Outline

• Motivation
• Description of Face Recognition System
• Overview
• Feature Extraction
• Haar Transform
• SVM
• Experiments
• Structure
• Data Set
• Results
• Conclusions

3
Motivation

• Very active field in CS right now.
• Applications in Security, Multimedia Retrieval,
  Human Computer Interaction,
• Many good algorithms exist
• Correlation Nearest Neighbor
• Eigenfaces PCA based
• Fisher Faces
• I am interested in doing real time face detection
  and feature extraction.

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Proposed System Overview

• Proposed a system Using cascaded SVM using Haar
  wavelet features with feature selection done by
  Adaboost.
• Combination of simple to complex classifiers
• Using SVMs for entire problem can lead to a lot
  useless computations on easily distinguishable
  background patters.
• Adaboost feature selection will cut out almost
  all background patterns quickly.
• The aim of boosting is to improve the
  classification of any given simple learning
  algorithm. (Schapire)
• Training with Adaboost can be very slow if used
  for the complete classification algorithm though.
  algorithm converges very slowly when examples
  become very hard.

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Feature Extraction

• Two general types of feature selection
• Filter Methods - preprocessing steps performed
  independently of the classification algorithm
• Wrapper Methods search though feature space
  using criterion of the classification algorithm
  to select optimal features
• 2 popular filter methods
• Haar Transform - simple
• Gabor Transform not simple

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Haar Feature Extraction

• Haar Wavelet
• Breaks down image into 4 sub-samples
• HH High passed in vertical and horizontal
  direction
• LH Low passed in vertical and high passed in
  horizontal
• HL High passed in vertical and low passed in
  horizontal
• LL Low passed in vertical and horizontal
  directions

LL
LH
LL
LH
LH
HL
HH
HL
HH
HL
HH
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Rectangle Features

• Rectangle Feature Examples
• horizontal
• vertical
• diagonal
• The sum of the pixels which lie in the white
  rectangles are subtracted from the sum pixels in
  the grey rectangles

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Haar Example
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SVMs

• The SVM determines the optimal hyperplane which
  maximizes the margin.
• The margin is the distance between the hyperplane
  and the nearest sample from the hyperplane.
• Decision function
• a is the solutions from quadratic programming
  problem
• Non-zero a is called a support vector

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Images

• Take some initial images for simple testing
• Either create or Find a large Database
• 2100 images of 2 people
• Yale Database B
• 5760 single light source images of 10 subjects
• under 576 viewing conditions (9 poses, shown in
  Figure (4), x 64 illumination conditions, shown
  in Figure (5)).

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Illumination
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Poses
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Experiments

• Initial Test
• No Feature Selection
• 100 64x64 training images
• 3072 length feature vector
• 2000 test images

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Training Results
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Test Results
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Yale Image Experiment 1

• Two subjects
• 512 Training Images
• 512 Test Images
• No feature selection
• 3072 length feature vector
• Linear SVM
• Training
• 18 support vectors
• No misclassified images
• Testing
• All images classified correctly

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Yale Image Experiment 2

• 10 test subjects
• 1024 faces
• 384 training faces
• Only 2 subjects trained
• 640 test faces
• 10 subjects tested
• Training
• 38 support vectors
• 0 misclassified
• Testing
• All faces positively classified very bad

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Yale Experiment 3

• Same setup as before but this time with feature
  extraction
• 1 level Haar transform
• 4 filtered images
• 3072 length feature vector
• No Feature Selection this time
• Training
• 20 support Vectors
• None misclassified
• Testing
• classification error rate was 0.020
• All positive labels classified correctly

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Training
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Testing
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Yale Experiment 4

• Same setup as 3 but with 2 level Haar Transform
• 2 level Haar transform
• 7 filtered images
• 3072 length feature vector
• No Feature selection this time
• Training
• 36 support Vectors
• None misclassified
• Testing
• classification error rate was 0.000
• Very good

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Training
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Testing
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Yale Experiment 5

• Same setup as 3 and 4 but now with feature
  selection
• Feature Selection Algorithm
• 1 level Haar Transform
• Sum 4 filtered images
• 4 features
• Training
• Nonlinear Support Vector (RBF)
• 372 support Vectors
• 9 misclassified
• Testing
• classification error rate was 0.2391
• Positive examples badly mislabeled

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Training
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Testing
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Yale Experiment 6

• Same setup as 5 but now with 16 selected features
• Feature Selection Algorithm
• 4 level Haar Transform
• Sum 16 filtered images
• 16 features
• Training
• Back to Linear Support Vector Machine
• 11 support Vectors
• 0 misclassified
• Testing
• classification error rate was 0.1812
• Positive examples labeled correctly

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Training
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Testing
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Conclusions

• Feature Extraction is simple but very powerful
• Better feature selection for better error rate
• Better rectangle filters
• Use Boosting
• Eliminate background patterns
• Reduce features
• Gabor Transform
• Better Testing Needed
• Test against known results
• Crop Images better
• Implement 3 layer system
• Feature Extraction
• Boosting (soft classier)
• SVMs (hard classifier)

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References

• 1 "Georghiades, A.S. and Belhumeur, P.N. and
  Kriegman, D.J.", "From Few to Many Illumination
  Cone Models for Face Recognition under Variable
  Lighting and Pose", "IEEE Trans. Pattern Anal.
  Mach. Intelligence", 2001, 23, 6, "643-660".
• 2 Le, Duy Dinh, Satoh S., Feature Selection by
  AdaBoost for SVM-Based Face Detection.
• 3 F. Smeraldi, O. Carmona, J. Bigün. Saccadic
  Search with Gabor features applied to Eye
  Detection and Real-Time Head Tracking (1998).
• 4 P. Viola and M. Jones. "Fast and Robust
  Classification using Asymmetric AdaBoost and a
  Detector Cascade"