Face Detection using the Spectral Histogram

representation

By: Christopher Waring, Xiuwen LiuDepartment 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:– 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• 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

• 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?

• 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

What is Spectral Histogram

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Types of Filters• 3 types of filters:

– Gradient Filters

– Gabor Filters

– Laplasian of Gaussians Filters

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Gibbs Sampling+ Simulated Annealing

• We want to sample from• We can use the induced Gibbs Distribution

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Face Synthesis usingGibbs Sampling + Simulated

Annealing

•A measure of the quality of the Representation

Comparison - PCA vs. Spectral Histogram

Original Image Reconstructed Images

Reconstruction vs. Sampling

Reconstruction sampling

Spectral Histograms of several images

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

Lighting correction

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

Adaptive Threshold

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• 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

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Spectral Histograms of several images

Algorithm I – Resultson CMU test set I

Method Detection Rate

False Detections

Waring & Liu 93.8% 94Yang, Ahuja & Kreigman 93.6% 74

Yang, Ahuja & Kreigman 92.3% 82

Yang Roth & Ahuja 94.2% 84Rowley, Baluja & Kanade 92.5% 862

Schneiderman 93.0% 88Colmenarz & Huang 98.0% 12758

Algorithm I – Resultson CMU test set II

Method Detection Rate

False Detections

Waring & Liu 89.4% 29Sung & Poggio 81.9 13

Rowley, Baluja & Kanade 90.3% 42

Yang, Ahuja & Kreigman 91.5% 1Yang, Ahuja & Kreigman 89.4% 3

Schneiderman 91.2% 12Yang Roth & Ahuja 93.6% 3

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

• 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

16,12,5,2T 150,120,90,60,30,0

Spectral Histograms of several images

Algorithm II (SVM) Results

Old Results

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 switch Histograms achieving 360 degrees invariance

Rotating 180 degrees

Combining the two classifiers

ResultsUpright test sets

ResultsRotated test sets

Rotation Invariation Results

More pictures

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)

• 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)

• 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