Automatic Image Anonymizer

1. Automatic Image Anonymizer Alex Brettingen James Esposito

2. Goals • Take any input image and remove, distort, or cover all human faces • Retain the original integrity of the input image

3. Step One: Detect Faces Viola-Jones Object (Face) Detection Framework Outlined here – http://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-IJCV-01.pdf

4. Viola - Jones Feature types and evaluation: • sums of image pixels within rectangular areas • four different types of features used in the framework: • value of any given feature is equal to the sum of the pixels within white rectangles subtracted from the sum of the pixels within dark rectangles

5. Viola - Jones Learning Algorithm • in a standard 24x24 pixel sub-window, there are 162,336 possible features • the Viola – Jones Algorithm employs a variant of the learning algorithm ‘AdaBoost’ to both select the best features and to train classifiers that use them.

6. Viola - Jones For this project, we used the Computer Vision Toolbox Matlab add-on to implement our Facial Detection (highly recommended) http://www.mathworks.com/products/computer-vision/

7. How accurate is the Algorithm?

8. How accurate is the Algorithm? IS IT A FACE?

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17. Anonymizer Now that we know that the algorithm is effective at detecting faces, we can find applications for it One such application is protecting the identities of people in photographs

18. Anonymizer We must alter the area of the photograph containing faces Blurring, covering entirely, or replacing with another image are possible methods

19. Method 1: Gaussian Blur

20. Method 1: Gaussian Blur

21. Method 1: Gaussian Blur

22. Method 1: Gaussian Blur

23. Method 2: Black-out

24. Method 2: Black-out

25. Method 3: Image Replacement

26. Anony–mice-er

27. Method 3: Image Replacement

28. Purrrrrrfect Anonymization

29. Remaining work • Smooth blur edges • Try a pixelation method • Blending Image Replacement

30. Questions?