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

2 / 35 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

3 / 35 About Us Team members: Gülsah Tümüklü (manager) Réka Juhász Emil Szimjanovszki Gergely Windisch

4 / 35 Goal Finding the terrorists Identifying faces even if they are disguised

5 / 35 System

6 / 35 Our Implementation Programmed in Matlab Input: RGB image Pre-processing output: Greyscale image Output: Yes/No (Terrorist-wise)

7 / 35 Things We Do Acquire an Image

8 / 35 Things We Do (2) Locate eyes

9 / 35 Things We Do (3) Normalise (rotate, scale, clip, put eyes to their place) 128*128

10 / 35 Things We Do (4) Face Recognition (details later)

11 / 35 Things We Do (n) Decide, then Call the police

12 / Useful Tips for Terrorists

13 / Useful Tips for Terrorists

14 / Useful Tips for Terrorists

15 / Useful Tips for Terrorists

16 / Useful Tips for Terrorists

17 / 35 Recognition Part Problem: Face Recognition Literature about Face Recognition Problems in Face Recognition Eigenfaces

18 / 35 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 / 35 Literature about Face Recognition Classification of Face Recognition Methods: Hollistic Methods Feature-Based Methods Hybrid Methods

20 / 35 Face Recognition Methods Hollistic Methods PCA Eigenfaces, Probabilistic eigenfaces, Fisherfaces/subspace LDA, SVM, Evolution pursuit, Feature lines, ICA Other Representations LDA/FLD, PDBNN

21 / 35 Face Recognition Methods(cont) Feature-Based Methods Pure geometry methods Dynamic link architecture Hidden Markov model Convolution Neural Network

22 / 35 Face Recognition Methods (cont.) Hybrid Methods Modular eigenfaces Hybrid LFA Shape-normalized Component-based

23 / 35 Problems in Face Recognition Feature Extraction Global Features Local Features Handling some problems: Illumination differences Facial expressions Occlusions pose

24 / 35 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 / 35 Eigenfaces (cont.) Trainning Part : calculate the Eigenfaces of datases Classification part : Reconstruct the test image and classify it

26 / 35 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 / 35 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 / 35

29 / 35 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

30 / 35 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

31 / 35 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

32 / 35 Results (1) – Training set Class 1: (Terrorists) Class 2: Class 3:

33 / 35 Results (2)

34 / 35 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)

35 / 35 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 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 , December B. Galamb: Color Based Eye Location