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 blum+@cs.cmu.edu. 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 blum+@cs.cmu.edu


  • 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)


  • 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



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 i results on cmu test set i
Algorithm I – Resultson CMU test set I

Algorithm i results on cmu test set ii
Algorithm I – Resultson CMU test set II

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

Results upright test sets
ResultsUpright test sets

Results rotated test sets
ResultsRotated test sets


  • 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