1

Terrorists

Face recognition of suspicious and (in most cases) evil

homo-sapiens

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The problem

Terrorists need to be identified when passing a security screen

Aim is positive identification of a few faces

Problem is that terrorists try to disguise themselves

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About Us

Team members: Gülsah Tümüklü (manager) Réka Juhász Emil Szimjanovszki Gergely Windisch

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Goal

Finding the terrorists Identifying faces even if they are

disguised

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System

Pre-processing(normalization,

rotation)

Training (eigenface)

Testing (Nearest

Neighbor)

Image Database

Test ImagePre-processing(normalization,

rotation)

Result

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Our Implementation

Programmed in Matlab Input: RGB image Pre-processing output: Greyscale

image Output: Yes/No (Terrorist-wise)

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Things We Do

Acquire an Image

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Things We Do (2)

Locate eyes

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Things We Do (3)

Normalise (rotate, scale, clip, put eyes to their place) 128*128

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Things We Do (4)

Face Recognition (details later)

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Things We Do (n)

Decide, then Call the police

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101 Useful Tips for Terrorists

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101 Useful Tips for Terrorists

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101 Useful Tips for Terrorists

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101 Useful Tips for Terrorists

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101 Useful Tips for Terrorists

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Recognition Part

Problem: Face Recognition Literature about Face Recognition Problems in Face Recognition Eigenfaces

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Problem: Face Recognition Identifying persons using some priori

information Many potential applications, such as

person identification, human-computer interaction, security systems, image retriveal systems, and finding terrorists

Stages of Face Recognition face detection feature extraction facial image classification

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Literature about Face Recognition

Classification of Face Recognition Methods: Hollistic Methods Feature-Based Methods Hybrid Methods

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Face Recognition Methods

Hollistic Methods PCA

Eigenfaces, Probabilistic eigenfaces, Fisherfaces/subspace LDA , SVM, Evolution pursuit, Feature lines, ICA

Other Representations LDA/FLD, PDBNN

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Face Recognition Methods(cont)

Feature-Based Methods Pure geometry methods Dynamic link architecture Hidden Markov model Convolution Neural Network

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Face Recognition Methods (cont.)

Hybrid Methods Modular eigenfaces Hybrid LFA Shape-normalized Component-based

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Problems in Face Recognition Feature Extraction

Global Features Local Features

Handling some problems: Illumination differences Facial expressions Occlusions pose

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Eigenfaces Firstly introduced by Pentland, and Turk in

1991 It is considered the first working facial

recognition technology Based on PCA Decompose face images into a small set of

characteristic feature images called eigenfaces

Eigenfaces may be thought of as the principal components of the original images

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Eigenfaces (cont.)

Trainning Part : calculate the Eigenfaces of datases

Classification part : Reconstruct the test image and classify it

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Calculation of Eigenfaces Calculate average face : v.

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.

The eigenvectors of covariance matrix C (M by M) give the eigenfaces. M is usually big, so this process would be time consuming.

TAAC

],...,,...,,...,[ 111

1 vuvuvuvuA pn

pn

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Calculation of Eigenfaces(cont.)

Use SVD Substract mean image from training images

diff.images=trainingimages-mean image Find the svd of diff.images [U S V] = svd(diff.images) The columns of U are automatically the e-vectors

of diff.images * diff.images’ Square of S gives eigenvalues

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Classifying a test Image Find the reconstructed image

Calculate weights First find difference test image Diff.test=test Image-mean Image Do inner product of each eigenimage with the

difference image to get a weight vector Find the reconstructed image for m=1:numTrainingImages reconstructionImage = reconstructionImage+(weight(m)*Eimage(:,:,m));

end

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Classifying a test Image (cont.)

If one of weighs is above a threshold, take the largest one and return that its owner also owns the new face.

Use nearest neighbor method Find minumum distance between

reconstructed image and eigenfaces and assign test image to class which has min distance

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Pros and Cons

Pros It is fast Efficiency Provides accurate recognition rates

Cons Very sensitive to occlusions,

illuminations, facial expression, pose Only works good with frontal faces

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Results (1) – Training set

Class 1:(Terrorists)

Class 2:

Class 3:

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Results (2)

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Conclusion

Face recognition is a difficult problem

Pre-processing is very important It is not enough to use only global

features Better results can be obtained with

different classifications (eigenfeatures)

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References M. Turk and A. Pentland. Eigenfaces for recognition. Journal of

Cognitive Neuroscience, 3 (1), 1991a. M. A. Turk and A. P. Pentland. Face recognition using

eigenfaces. In Proc. of Computer Vision and Pattern Recognition, pages 586-591. IEEE, June 1991b.

W. Zhao, R. Chellappa, P. J. Phillips and A. Rosenfeld, "Face Recognition :  A Literature Survey", ACM Computing Surveys(CSUR), vol. 35, issue 4, pp. 399-458, December 2003.

http://cilek.ceng.metu.edu.tr/facedetect B. Galamb: Color Based Eye Location