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InSight : Recognizing Humans without Face Recognition

InSight : Recognizing Humans without Face Recognition. He Wang , Xuan Bao , Romit Roy Choudhury , Srihari Nelakuditi. Motivation – Application Scenarios. s hare a ride to airport. Elle. Bret. Bob. 2. Overview – Self-Fingerprints. Cloud. Self-Fingerprints. Bob. Bret. Elle.

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InSight : Recognizing Humans without Face Recognition

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  1. InSight: Recognizing Humans without Face Recognition He Wang, XuanBao, Romit Roy Choudhury, SrihariNelakuditi

  2. Motivation – Application Scenarios share a ride to airport Elle Bret Bob 2

  3. Overview – Self-Fingerprints Cloud Self-Fingerprints Bob Bret Elle John

  4. Overview – Recognition Cloud Glass

  5. Overview – Recognition Cloud Glass

  6. Overview – Recognition Cloud Glass

  7. Challenges • Perspectives are different • Clothes have wrinkles • Lighting conditions change

  8. Extracting Fingerprints Colors Spatiograms Patterns Wavelets

  9. Extracting Fingerprints – Colors Spatiograms Color Conversion RGB HSV

  10. Extracting Fingerprints – Colors Spatiograms color histogram spatial distribution RGB HSV

  11. Extracting Fingerprints – Patterns Wavelets Wavelet sub-bands: vertical, horizontal and diagonal dimensions.

  12. Fingerprint Matching Cloud MatchingSpatiograms S = {n, µ, σ} cloud S = {n’, µ’, σ’} glass Similarity = spatial distributions color histograms Glass

  13. Fingerprint Matching Cloud MatchingWavelets W= {f1, f2, f3,…} cloud W= {f1’, f2’, f3’,…} glass Bagged Decision Tree (BDT) Glass

  14. Evaluation Setting • PivotHead glass captured users from the front. • 15 users was dressed in their regular attires. • Users actively used their smartphones. • Phone opportunistically took “profile” pictures of the user.

  15. Evaluation - Matching Color Spatiograms front

  16. Evaluation - Matching Wavelets of Patterns front

  17. Evaluation – Combining Colors and Patterns Evaluation – front

  18. Evaluation – Performance with Self-Fingerprints • Matching front view front

  19. Self-fingerprints may not be Sufficient • Clothes’ difference is not captured when clothes are similar? • Clothes have different colors/patterns at the back? Paul Dan Bret Bret

  20. Refining the Self-Fingerprint (Similar Clothes) Cloud refining self-fingerprint John Bob Dan Glass

  21. Refining the Self-Fingerprint (Similar Clothes) Cloud Can recognize after refinement! Dan Paul Glass

  22. Refining the Self-Fingerprint Dan = {F1} F1 F2 Dan = {F1, F2} F2 F3 Dan = {F1, F2, F3} Dan

  23. Refining the Self-Fingerprint (Back View) Cloud refining back view fingerprint Bret Glass

  24. Evaluation – Performance with Self-Fingerprints • Matching back view back

  25. Evaluation – Performance with Refined Fingerprints • Matching back view after refining fingerprints back

  26. Discussion • Privacy of opportunistic pictures • User consent before uploading • Overlapping users in view • Fingerprint refinement helps • Incremental deployment with some non-participants • More time and mobility help • Cloud vsp2p • Different trade-offs

  27. Conclusion • Colors and patterns on clothes help fingerprint humans. • Preliminary evaluation with 15 people provides promising results. • Other type of fingerprints exists such as motion.

  28. Anyone for beer after this talk? Looking for interns New Primitive for Broadcasting to Visible Vicinity. 29

  29. Questions, Comments? Thank You

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