1 terrorists face recognition of suspicious and (in most cases) evil homo-sapiens
TRANSCRIPT
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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