International Workshop ISDSI 09 June 25th, 2009, Camogli

1. International Workshop ISDSI 09June 25th, 2009, Camogli A Framework for Privacy-Preserving Face Matching Authors: Filippo Volpi, Monica Scannapieco, Tiziana Catarci filvolpi@gmail.com scannapi@istat.it catarci@dis.uniroma1.it

2. 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

3. To find the matching images 2 faces database Frontal photos To preserve privacy towards P and Q towards W Approximate matching matching matching Specifications

4. 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…

5. 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

6. Parties P and Q: Agreement on parameters Normalization Feature extraction Trasposition Third party W: Comaprison Send results Protocol architecture

7. Image dimensions Affine transformation Eyes coordinates Mouth coordinate Masking Eyes and mouth Eyes Normalization

8. Extraction: Local Binary Pattern • Local method • pixel window • variable threshold • Labeling the image • Building the histograms • In case weighted • Dimensionality reduction • uniform patterns

9. 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

10. 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

11. 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

12. 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

13. Evaluation of results • Average of precision and recall • Minimum between precision and recall (guarantee) • F-measure

14. 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

15. Results • Best parameters: • LBP(16,2) • No mask • “Eyes” normalization • Use of all the patterns

16. 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.

17. International Workshop ISDSI 09June 25th, 2009, Camogli A Framework for Privacy-Preserving Face Matching Authors: Filippo Volpi, Monica Scannapieco, Tiziana Catarci filvolpi@gmail.com scannapi@istat.it catarci@dis.uniroma1.it