International workshop isdsi 09 june 25th 2009 camogli
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International Workshop ISDSI 09 June 25th, 2009, Camogli. A Framework for Privacy-Preserving Face Matching. Authors: Filippo Volpi, Monica Scannapieco, Tiziana Catarci. [email protected] [email protected] [email protected] Scenario.

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International Workshop ISDSI 09 June 25th, 2009, Camogli

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International workshop isdsi 09 june 25th 2009 camogli

International Workshop ISDSI 09June 25th, 2009, Camogli

A Framework for

Privacy-Preserving Face Matching

Authors: Filippo Volpi, Monica Scannapieco, Tiziana Catarci

[email protected] [email protected] [email protected]


Scenario

Scenario

  • 3 parties protocol

    • Parties P and Q => databases

    • Third party W => comparison

  • Face Recognition

    • To identify images representing the same person

  • Feature Extraction

    • To extract the features of images

    • Comparison through similarity metric

  • Privacy-preserving

    • P can’t observe Q’s data and viceversa


Specifications

To find the matching images

2 faces database

Frontal photos

To preserve privacy

towards P and Q

towards W

Approximate matching

matching

matching

Specifications


Real word examples

Real word examples

  • Sicurity in a bank

    • Video surveillance at entrance

    • Governmental database of criminals

    • Comparison among customers’ and database’s photos

    • Certified bank sicurity

    • Certified customers’ privacy

  • Comparison between organizations’ databases

  • Sensitive targets, crowded places, etc…


Issues

Issues

  • Images

    • High-dimensionality input

    • Noise

  • One sample problem

  • Face recognition sensible to:

    • Changes of illumination, pose, expression…

    • Passing of time

  • No bijection person-image

  • No formal models, no metrics for calculating distances between images of people


Protocol architecture

Parties P and Q:

Agreement on parameters

Normalization

Feature extraction

Trasposition

Third party W:

Comaprison

Send results

Protocol architecture


Normalization

Image dimensions

Affine transformation

Eyes coordinates

Mouth coordinate

Masking

Eyes and mouth

Eyes

Normalization


Extraction local binary pattern

Extraction: Local Binary Pattern

  • Local method

    • pixel window

    • variable threshold

  • Labeling the image

  • Building the histograms

    • In case weighted

  • Dimensionality reduction

    • uniform patterns


Transposition and comparison

Transposition and comparison

  • Similarity function

    • weighted Chi square

  • Transposition

    • Cipher for rows, columns, groups, ecc...

    • Distance-preserving

    • Unvaried performance

    • Certified privacy

S and M: histograms to be compared

wj: weight of region j


Security analysis

Security analysis

  • Honest W

    • P and Q never comunicate or share data

    • W returns to each party only a subset of that party’s data


Security analysis 2

Security analysis (2)

  • Honest-but-curious W

    • LBP: unreversable operator

      • Changeable windows’ threshold

      • Unique label for not-uniform patterns

      • Spatial information only at the regional level

    • Against statistical analysis

      • No use of weights

      • Transposition cipher


Parameters

Parameters

  • Agreement between parties

    • Normalized images’ dimensions

    • Eyes and mouth coordinates

    • Number of regions

  • Structural

    • Operator LBP (samples, radius)

    • Interpolation

    • Masked or not

    • Weighted or not

    • Normalization

    • All the patterns or only the uniforms


Evaluation of results

Evaluation of results

  • Average of precision and recall

  • Minimum between precision and recall (guarantee)

  • F-measure


Experiments

Experiments

  • Dataset ⊆FERET, |DATASET| = 2˙402

  • Phase 1

    • Exploration of 4×4×2×2×2×2 = 256 combinations of parameters

  • Phase 2

    • Conjunctive local search

    • Refining of results

  • Phase 3

    • Disjunctive local search


Results

Results

  • Best parameters:

    • LBP(16,2)

    • No mask

    • “Eyes” normalization

    • Use of all the patterns


Future works

Future works

  • To estimate the impact of different dimensions and different number of regions on performance and efficiency

  • Regions of different shapes and dimensions depending on the importance of the facial features contained in the region

  • Techniques for further dimensionality reduction

  • Generalization of the privacy-preservation protocol (more than two parties, …)

Portions of the research in this paper use the FERET database of facial images collected under the FERET program, sponsored by the DOD Counterdrug TechnologyDevelopmentProgram Office.


International workshop isdsi 09 june 25th 2009 camogli1

International Workshop ISDSI 09June 25th, 2009, Camogli

A Framework for

Privacy-Preserving Face Matching

Authors: Filippo Volpi, Monica Scannapieco, Tiziana Catarci

[email protected] [email protected] [email protected]


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