De Identification Seminar

Person De-Identification

in Videos

Guided By :-Ms. Mamata NayakAsst. Professor

Submitted By :-NikitaRegd. No. – 1241017021Csit ‘C’

Department of Computer Science and Information TechnologyInstitute of Technical Education and Research,

BBSR Siksha ’O’ Anusandhan University

Proposed Approach

Recognition vs de-identification

De-Identification: General Framework

Conclusions

Outline :

Introduction

WHY De-Identification ??

Videos over the internet invades our privacy. HOW?? E.g. Google Street View

As the number of video surveillance systems increases, ensuring privacy gets important.

WHY De-Identification ??

May exist the need to see the individuals in them, identifying the action suffices in most cases.

The actor needs to be identified only rarely and only to authorized personnel.

Hence, De-Identification !!

Recognition vs. De-Identification: Recognition & de-identification are opposite to each other.

Recognition De-identification

Reco

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De-Id

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makes use of all possible features to identify a person.

prevents a person’s identity from being connected with information to thwart recognition

De-Identification: General Framework Easy to hide the identity of individuals by replacing

a conservative area around them by, say, black pixels.

Goal is to protect the privacy of the individuals while

providing sufficient feel for the human activities in the

space being imaged.

Privacy protection provided should be immune to recognition

using computer vision as well as using human.

A. Different Scenarios & De-identificationThree types of videos:

1. Casual videos: captured for other purposes and gets shared.

2. Public Surveillance videos: come from cameras watching spaces such as airports, streets and so on.

3. Private surveillance videos: cameras placed at the entrances

of semi-private spaces like offices.

B. Criteria for De-identificationFeatures to recognize humans:

1. Face plays a dominant role in automatic and manual

identification.

2. The body silhouette and the gait are important clues

available in videos.

3. Race and gender, hard to mask completely.

C. Subverting De-identification

de-identification can be “attacked” to reveal the identity of individuals involved

1. Reversing the de-identification transformation.

2. Recognizing persons from face, body outline, gait etc.

C. Subverting De-identification (contd.)de-identification can be “attacked” to reveal the identity of individuals involved

3. Manual identification is another way to subvert

de-identification, though it is considerably more

expensive.

4. Brute-Force verification is another way to attack.

D. Storage of Videos1. The de-identification should be selectively reversed

when needed. 2. Safest approach is to de-identify the video at the capture-camera. Only the transformed video is recorded.

3. Another approach is to store the original video, with

sufficiently hard encryption, along with the de-identified

video.

De-identification Model

The system is comprised of three modules:

1. Detect & track2. Segmentation

3. De-Identification

A. Detect & Track

1. The first step is to detect the presence of a person in the scene.

2. Patch-based recognition approach for object tracking.

3. To avoid errors resulting from fast changing scale, we apply the human detector every ‘F’ frames

4. The output of the human detector becomes the input to the

tracking module. The value of F depends on the amount

of movement in the video.

B. Segmentation

1. The faces of the human in every frame, are stacked across time to generate a video tube of the person.

2. Multiple video tubes are formed if there are multiple people in the video.

3. Rigid but blocky outline of the human is obtained after segmentation.

C. De-identification

Two de-identification transformations:

1. Exponential blur of pixels.

2. Line Integral Convolution (LIC).

Exponential blur of pixels

Blurring of the segmented area by continuous overlapping till person’s identity gets hidden.

Suitable only where gait is not involved.

Conclusion Presented a basic system to protect privacy against human recognition

Issues relating to de-identification of individuals in videos to protect their privacy were analyzed

Characteristics are difficult to hide if familiarity is high to the user

Blurring is a good way to hide the identity if gait is not involved.

Bibliography

[1] “De-Identification.” Wikipedia: The free Encyclopedia. Wikipedia Foundation, Inc. 12th June 2015. Web. 23 Jul 15. < https://en.wikipedia.org/wiki/De-identification>

[2] P. Agrawal and P. J. Narayanan, “Person De-Identification in Videos”, IEEE Transactions on circuits and systems for video technology, Vol. 21, No. 3, pp. 299-310, Mar. 2011