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Terrorists

Terrorists. Face recognition of suspicious and (in most cases) evil homo-sapiens. The problem. T errorists 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. About Us. Team members:

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Terrorists

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  1. Terrorists Face recognition of suspicious and (in most cases) evil homo-sapiens

  2. The problem • Terrorists need to be identified when passing asecurity screen • Aim is positive identification of a few faces • Problem is that terrorists try to disguise themselves

  3. About Us • Team members: • Gülsah Tümüklü (manager) • Réka Juhász • Emil Szimjanovszki • Gergely Windisch

  4. Goal • Finding the terrorists • Identifying faces even if they are disguised

  5. System

  6. Our Implementation • Programmed in Matlab • Input: RGB image • Pre-processing output: Greyscale image • Output: Yes/No (Terrorist-wise)

  7. Things We Do • Acquire an Image

  8. Things We Do (2) • Locate eyes

  9. Things We Do (3) • Normalise (rotate, scale, clip, put eyes to their place) • 128*128

  10. Things We Do (4) • Face Recognition (details later)

  11. Things We Do (n) • Decide, then Call the police

  12. 101 Useful Tips for Terrorists

  13. 101 Useful Tips for Terrorists

  14. 101 Useful Tips for Terrorists

  15. 101 Useful Tips for Terrorists

  16. 101 Useful Tips for Terrorists

  17. Recognition Part • Problem: Face Recognition • Literature about Face Recognition • Problems in Face Recognition • Eigenfaces

  18. 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

  19. Literature about Face Recognition • Classification of Face Recognition Methods: • Hollistic Methods • Feature-Based Methods • Hybrid Methods

  20. Face Recognition Methods • Hollistic Methods • PCA • Eigenfaces, Probabilistic eigenfaces, Fisherfaces/subspace LDA , SVM, Evolution pursuit, Feature lines, ICA • Other Representations • LDA/FLD, PDBNN

  21. Face Recognition Methods(cont) • Feature-Based Methods • Pure geometry methods • Dynamic link architecture • Hidden Markov model • Convolution Neural Network

  22. Face Recognition Methods (cont.) • Hybrid Methods • Modular eigenfaces • Hybrid LFA • Shape-normalized • Component-based

  23. Problems in Face Recognition • Feature Extraction • Global Features • Local Features • Handling some problems: • Illumination differences • Facial expressions • Occlusions • pose

  24. 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

  25. Eigenfaces (cont.) • Trainning Part : calculate the Eigenfaces of datases • Classification part : Reconstruct the test image and classify it

  26. 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.

  27. 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

  28. 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

  29. 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

  30. 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

  31. Results (1) – Training set Class 1: (Terrorists) Class 2: Class 3:

  32. Results (2)

  33. 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)

  34. 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

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