1. Face recognition Eigenfacesmethod and the image normalizationitrequires

2. Introduction

3. Face recognition Database of faces • Face to recognize

4. Classicapproach (1) Picture normalization Pixel-by-pixel comparison matchinglevel µ

5. Classicapproach (2) • Matchinglevelscomparison second case first case µ1 argmaxi(µi) < threshold argmaxi(µi) > threshold µ2 µ3 µ4 Face recognized ! Face unknown…. µ5 ?

6. First stake • Pre-processing Scaling-resizing Cropping Rotating Aligning

7. First stake • Pre-processing • Whatoperations have to beperformed ? • In whatorder ?

8. Second stake • Computation • Pixel-by-pixel comparison • 75 000 computations for each pair of images ! for 250x300 images (small size)

9. Second stake • Computation • How to reduce the number of computations ?

10. Having the right input for Eigenfacesmethod Pre-processing

11. Edgehighlighting • Grey-level thresholding • Three functions to find coordinates of the top, left and right sides of the face

12. Detect face rotation

13. Coarse cropping The coordinates of the top, left and right sides (red dots) Then, cropping the original image accordingly

14. Alternative approach : skin detection Detectionbased on RGBlevels -log(R/G) and –log(B/G) Comparisonwith gray-levelcropping ? • More accurrate • No input parameterisnecessary (λthreshold)

15. How to localize the eyes? Face re-scaling Calculation of the distance between the two eyes and normalization after eye detection Re-cropping so that all pictures from the database have the same size The input for eigenfaces

16. Eyelocalization : steps • Converttheimagefromrgbtoycbcrformat • Calculatetheinfluencesofthe different channelsandcombinethem • Find thecenterbetweenthetwoeyes

17. Calculatingthe different influences • CbandCrchannelsarenormalizedindividuallybetween [0,255] • Chrominancecontributioniscalculatedusing: • Luminancecontributioniscalculatedusing: • BotharecombinedbyMultiplyingthemelement-wise

18. Calculatingthecolorcontribution

19. Calculatingthecontrastcontribution dilation

20. Combiningthetwo Images

21. Calculatingthecenteroftheeyes • Forthecalculationofthecenteroftheeyeswesplitthefacepicture in twohalfs • Bycalculatingthe „centerofmass“ themiddlepointsforeachsidecanbefound x y x

22. Computingless to reach the sameresult EigenfacesMethod

23. A dimensional issue (1) N N²x 1 vector N N x N image High-dimensionalspace !

24. Eingenfacesidea Changing to a lower-dimensionalspace Reduction keep the mostsignificantcommonfeatures N²-dimensionalspace M-dimensionalspace M << N²

25. Eigenfacesalgorithm (1) M images in the database … N² N² x M matrix M

26. Eigenfacesalgorithm (2) Consider the covariance matrix AAT Image-matrix A TAA is a N² x N² matrix Eigenvectors and eigenvalues ? … N² Compute the matrix ATA ATA is a M x M matrix Sameeigenvectors/values as TAA ! Sort the values M

27. The Eigenfaces N² x M eigenfaces-matrix Most significantcommonfeatures of the faces … N² M

28. Representing the faces onto this basis N² x M image-matrix … N² M

29. The first 24 eigenfaces

30. Recognizing a face (1) Projecting the unknown face onto the eigenfaces basis

31. Recognizing a face (2) Computing the distances between the unknown face and the database face Use of the eigenfacescoordinates (M coordinatesinstead of N²)

32. Someexamples

33. Results

34. First results Withourdatabase With an internet databse Eigenfaces recognition 70% Face edgesdetection 100% precision ? Eyedetection : 70%success (7/10)

35. Limitations and improvements Eigenfaces add face detection check the accurracy of the matching (threshold) check whether the methodstillworkswithdifferent face orientations Pre-processing deal with non- pure white backgrounds differentcropping for eyedetection and eigenfacesmethod Integration have only one functionning program