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Privacy and Intelligibility through Pixellation and Edge Detection Prof. Atta Badii, Mathieu Einig
School of Systems Engineering University of Reading, UKWWW: http://www.isr.reading.ac.ukeMAIL: [email protected]
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Introduction
• Privacy protection by visual anonymisation
• Two main challenges:
- Detecting faces
- Filtering faces
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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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Face Detection
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Face Detection
• Algorithms comparison:
LBP Cascade
Histogram of Oriented Gradient
Speed + -Long distance - +
Medium distance + +Short distance + =
Light Invariance - +Occlusion Invariance
- +
Front/back discrimination
+ -
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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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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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Face Filtering
• Privacy through pixellation
- Faces reduced to 12x12 pixels
- Additional scrambling with median blur
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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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Face Filtering
• Merging of the two filters
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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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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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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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Results: Subjective Evaluation
• Results:
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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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Thank you
Atta BadiiIntelligent Systems Research Lab (ISR)
School of Systems EngineeringUniversity of Reading
Whiteknights RG6 6AY UKPhone: 00 44 118 378 7842
Fax: 00 44 118 975 [email protected], www.ISR.reading.ac.uk