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Privacy and Intelligibility through Pixellation and Edge Detection                    Prof. Atta Badii, Mathieu Einig     ...
Introduction• Privacy protection by visual anonymisation• Two main challenges:   - Detecting faces   - Filtering faces    ...
Face Detection• LBP Face Detector from OpenCV   - Extremely fast   - Good results for close-up frontal faces• Histogram of...
Face Detection                 4
Face Detection• Algorithms comparison:                                   Histogram of Oriented                     LBP Cas...
Face Detection• Combination   - Good in most situations   - Cannot differentiate between front and back in some     cases•...
Face Detection• Front/back discrimination:   - If LBP detector triggered, it is a frontal face   - If not       • Assume t...
Face Filtering• Privacy through pixellation   - Faces reduced to 12x12 pixels   - Additional scrambling with median blur  ...
Face Filtering• Intelligibility through edge detection   - Sobel filter on the saturation component of the image   - Satur...
Face Filtering• Merging of the two filters                                10
Results: Objective Evaluation• Accuracy   - Overlap between the detected faces and the manual      annotation• Anonymity  ...
Results: Objective Evaluation• ResultsCriteria             ScoreAccuracy             0.50 ± 0.19Anonymity            1.00 ...
Results: Subjective Evaluation• Questionnaire   - Subjects’ accessories   - Subjects’ gender   - Subjects’ ethnicity   - R...
Results: Subjective Evaluation• Results:                                     14
Conclusion• Privacy protected to some extent   - One misdetection gives away too much information on   the person   - Bett...
Thank you                                     Atta Badii              Intelligent Systems Research Lab (ISR)              ...
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MediaEval 2012 Visual Privacy Task: Privacy and Intelligibility through Pixellation and Edge Detection

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MediaEval 2012 Visual Privacy Task: Privacy and Intelligibility through Pixellation and Edge Detection

  1. 1. Privacy and Intelligibility through Pixellation and Edge Detection Prof. Atta Badii, Mathieu Einig School of Systems Engineering University of Reading, UK WWW: http://www.isr.reading.ac.uk eMAIL: atta.badii@reading.ac.uk
  2. 2. Introduction• Privacy protection by visual anonymisation• Two main challenges: - Detecting faces - Filtering faces 2
  3. 3. Face Detection• LBP Face Detector from OpenCV - Extremely fast - Good results for close-up frontal faces• Histogram of Oriented Gradients - Trained for detecting upper bodies 3
  4. 4. Face Detection 4
  5. 5. Face Detection• Algorithms comparison: Histogram of Oriented LBP Cascade Gradient Speed + - Long distance - + Medium distance + + Short distance + = Light Invariance - + Occlusion - + Invariance Front/back + - discrimination 5
  6. 6. Face Detection• Combination - Good in most situations - Cannot differentiate between front and back in some cases• Tracking - Hungarian algorithm • Matching made on position and size of the face - Faces kept even when lost • Face position extrapolated for a few frames • Duration depends on the number of previous detections 6
  7. 7. Face Detection• Front/back discrimination: - If LBP detector triggered, it is a frontal face - If not • Assume that people looking at the camera are moving towards it • Use tracker to analyse the position and size of the faces - HMM trained for 3 scenarios: » Moving towards the camera » Standing still » Moving away from the camera - Anonymisation is required only for the 2 first cases 7
  8. 8. Face Filtering• Privacy through pixellation - Faces reduced to 12x12 pixels - Additional scrambling with median blur 8
  9. 9. Face Filtering• Intelligibility through edge detection - Sobel filter on the saturation component of the image - Saturation component is the most ‘robust’ in different lighting conditions 9
  10. 10. Face Filtering• Merging of the two filters 10
  11. 11. Results: Objective Evaluation• Accuracy - Overlap between the detected faces and the manual annotation• Anonymity - Ratio of faces that could no longer be detected after filtering• Intelligibility - Number of people detected even after filtering• Similarity - SSIM and PSNR scores 11
  12. 12. Results: Objective Evaluation• ResultsCriteria ScoreAccuracy 0.50 ± 0.19Anonymity 1.00 ± 0.00Intelligibility 0.93 ± 0.06SSIM 0.96 ± 0.02PSNR 35.80 ± 1.07 12
  13. 13. Results: Subjective Evaluation• Questionnaire - Subjects’ accessories - Subjects’ gender - Subjects’ ethnicity - Rating the perceived effectiveness of privacy protection - Rating the level of perceived irritation/distraction from the filter - Recognising filtered faces from a list of clear faces 13
  14. 14. Results: Subjective Evaluation• Results: 14
  15. 15. Conclusion• Privacy protected to some extent - One misdetection gives away too much information on the person - Better face detection is crucial• Irritation/distraction need to be addressed 15
  16. 16. Thank you Atta Badii Intelligent Systems Research Lab (ISR) School of Systems Engineering University of Reading Whiteknights RG6 6AY UK Phone: 00 44 118 378 7842 Fax: 00 44 118 975 1994 atta.badii@reading.ac.uk, www.ISR.reading.ac.uk 16

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