face detection using the spectral histogram representation
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Face Detection using the Spectral Histogram representation. By: Christopher Waring, Xiuwen Liu Department of Computer Science Florida State University. Presented by: Tal Blum [email protected] Sources. The presentation is based on a few resources by the authors:

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face detection using the spectral histogram representation

Face Detection using the Spectral Histogram representation

By: Christopher Waring, Xiuwen Liu

Department of Computer Science

Florida State University

Presented by:

Tal Blum [email protected]

sources
Sources
  • The presentation is based on a few resources by the authors:
    • Exploration of the Spectral Histogram for Face Detection – M.Sc thesis by Christopher Waring (2002)
    • Spectral Histogram Based Face Detection – IEEE (2003)
    • Rotation Invariant Face Detection Using Spectral Histograms & SVM – CVPR submission
    • Independent Spectral Representation of images for Recognition – Optical Society of America (2003)
overview
Overview
  • Spectral Histogram
    • Overview of Gibbs Sampling + Simulated annealing
  • Method for Lighting Normalization
  • Data used
  • 3 Algorithms
    • SH + Neural Networks
    • SH + SVM
    • Rotation Invariant SH +SVM
  • Experimental Results
  • Conclusions & Discussions
two approaches to object detection
Two Approaches to Object Detection
  • Curse of dimensionality
    • Features should be: (Vasconcelos)
      • Independent
      • have low Bayes Error
  • 2 main Approaches in Object Detection:
    • Complicated Features with many interactions
      • Require many data points
      • Use syntactic variations that mimic the real variations
      • Estimation Error might be high
      • Assuming Model or Parameter structure
    • Small set of features or small number of values
      • This is the case for Spectral Histograms
      • The Bayes Error might be high (Vasconcelos)
      • Estimation Error is low
why spectral histograms
Why Spectral Histograms?
  • Translation Invariant
    • Therefore insensitive to incorrect alignment.
  • (surprisingly) seem to be able to separate Objects from Non-Objects well.
  • Good performance with a very small feature set.
  • Good performance with a large rotation invariance.
  • Don’t rely at all on any global spatial information
  • Combining of variant and invariant features
  • Will play a more Important role
types of filters
Types of Filters
  • 3 types of filters:
    • Gradient Filters
    • Gabor Filters
    • Laplasian of Gaussians Filters

The exact composition of the filters is different for each algorithm.

gibbs sampling simulated annealing
Gibbs Sampling+ Simulated Annealing
  • We want to sample from
  • We can use the induced Gibbs Distribution
  • Algorithm:
  • Repeat
    • Randomly pick a location
    • Change the pixel value according to q
  • Until for every filter
face synthesis using gibbs sampling simulated annealing
Face Synthesis usingGibbs Sampling + Simulated Annealing
  • A measure of the quality of the Representation
reconstruction vs sampling
Reconstruction vs. Sampling

Reconstruction

sampling

lighting correction
Lighting correction
  • They use a 21x21 sized images
  • Minimal brightness plane of 3x3 is computed from each 7x7 block
  • A 21x21 correction plane is computed by bi-linear interpolation
  • Histogram Normalization is applied
detection post processing
Detection & Post Processing
  • Detection is don on 3 scaled Gaussian pyramid, each scale down sampled by1.1
  • detections within 3 pixels are merged
  • A detection is marked as final if it is found at at least two concurrent levels
  • A detection counts as correct if at least half of the face lies within the detection window
algorithm i using a neural network
Algorithm Iusing a Neural Network
  • Neural Network was used as a classifier
    • Training with back propagation
  • Data Processing
    • 1500 Face images & 8000 Non-Face images
    • Bootstrapping was used to limit the # non faces

(Sung Poggio) leaving 800 Non-Faces

  • Use 8 filters with 80 bins in each
alg i filter selection
Alg. I - Filter Selection
  • 7 LoG filters with
  • 4 Difference of gradient: Dx Dy Dxx Dyy
  • 70 Gabor filters with:
    • T = 2,4,6,8,10,12,14
    • = 0,40,80,120,160,200,280,320
  • Selected Filters (8 out of 81)
  • 4 LoG filters with:
  • 3 Difference of Gradiant: Dx Dxx & Dyy
  • 1 Gabor filter with T=2 and
algorithm ii using a svm
Algorithm IIusing a SVM
  • SVM instead of a Neural Network
  • They use more filters
    • 34 filters (instead of 7)
    • 359 bins (instead of 80)
  • 4500 randomly rotated Face images & 8000 Non-Face images from before
algorithm ii svm filters
Algorithm II (SVM)Filters
  • The filters were hand picked
  • Filters:
    • The Intensity filter
    • 4 Difference of Gradient filters Dx,Dy,Dxx &Dyy
    • 5 LoG filgers
    • 24 gabor filters with
  • Local & Global Constraints
  • Using Histograms as features
algorithm iii using svm rotation invariant features
Algorithm IIIusing SVM +rotation invariant features
  • Same features as in Alg. II
  • The Features enable 180 degrees of rotation invariance
  • Rotate the image 180 degrees and switchHistograms achieving 360 degrees invariance
conclusions
Conclusions
  • A system which is rotation & translation invariant
  • Achieves very high accuracy for frontal faces and rotated frontal faces
  • The system is not real time, but is possible to implement convolution in hardware
  • Uses limited amount of data
  • Accuracy as a function of efficiency
conclusions 2
Conclusions (2)
  • Faces are identifiable through local spatial dependencies where the global ones can be globally modeled as histograms
  • The problem with spatial methods is the estimation of the parameters
  • The SH representation is independent of classifier choice
  • SVM outperforms Neural Networks
  • The Problems and the Errors of this system are considerably different than of other systems
conclusions 3
Conclusions (3)
  • Localization in Space and Scale is not as good as other methods
  • Translation Invariant features can enable a coarser sampling the image
  • Use adaptive thresholding
  • Use several scales to improve performance
  • SH can be used for sampling of objects
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