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: atta.badii@reading.ac.uk

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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 1994atta.badii@reading.ac.uk, www.ISR.reading.ac.uk