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. Introduction
• Privacy protection by visual anonymisation
• Two main challenges:
- Detecting faces
- Filtering faces
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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
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4. Face Detection
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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
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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
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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
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8. Face Filtering
• Privacy through pixellation
- Faces reduced to 12x12 pixels
- Additional scrambling with median blur
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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
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10. Face Filtering
• Merging of the two filters
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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
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12. Results: Objective Evaluation
• Results
Criteria Score
Accuracy 0.50 ± 0.19
Anonymity 1.00 ± 0.00
Intelligibility 0.93 ± 0.06
SSIM 0.96 ± 0.02
PSNR 35.80 ± 1.07
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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
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14. Results: Subjective Evaluation
• Results:
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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
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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
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