1 / 44

Faces in the Wild

Faces in the Wild. Detection, Alignment and Recognition of Real World Faces Erik Learned-Miller with Vidit Jain, Gary Huang, Andras Ferencz, et al. Is Face Recognition Solved?. Is Face Recognition Solved?. “100% Accuracy in Automatic Face Recognition” [!!!]. Science 25 January 2008.

smohammed
Download Presentation

Faces in the Wild

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Faces in the Wild Detection, Alignment and Recognition of Real World Faces Erik Learned-Miller with Vidit Jain, Gary Huang, Andras Ferencz, et al.

  2. Is Face Recognition Solved?

  3. Is Face Recognition Solved? “100% Accuracy in Automatic Face Recognition” [!!!] Science 25 January 2008

  4. Is Face Recognition Solved? “100% Accuracy in Automatic Face Recognition” [!!!] Science 25 January 2008 A history of overstated results.

  5. The Truth • Many different face recognition problems • Out of context, accuracy is meaningless! • Many problems are REALLY HARD! • For some problems state of the art is 70% or worse! • We have a long way to go!

  6. Face Recognition at UMass • Labeled Faces in the Wild • The Detection-Alignment-Recognition pipeline • Congealing and automatic face alignment • Hyper-features for face recognition • New directions in recognition

  7. Labeled Faces in the Wild http://vis-www.cs.umass.edu/lfw/

  8. The Many Faces of Face Recognition Labeled Faces in the Wild

  9. The Many Faces of Face Recognition Labeled Faces in the Wild

  10. The Many Faces of Face Recognition Labeled Faces in the Wild

  11. The Many Faces of Face Recognition Labeled Faces in the Wild

  12. The Many Faces of Face Recognition Labeled Faces in the Wild

  13. Labeled Faces in the Wild • 13,233 images, with name of each person • 5749 people • 1680 people with 2 or more images • Designed for the “unseen pair matching problem”. • Train on matched or mismatched pairs. • Test on never-before-seen pairs. • Distinct from problems with “galleries” or training data for each target image. • Best accuracy: currently about 73%!

  14. Detection-Alignment-Recognition Pipeline Detection Alignment Recognition “Same”

  15. Detection-Alignment-Recognition Pipeline Detection Alignment Recognition “Same” Parts should work together.

  16. Labeled Faces in the Wild • All images are output of a standard face detector. • Also provides aligned images. • Consequence: any face recognition algorithm that works well on LFW can easily be turned into a complete system.

  17. Congealing (CVPR 2000)

  18. Criterion of Joint Alignment • Minimize sum of pixel stack entropies by transforming each image. A pixel stack

  19. Congealing Complex Images Window around pixel SIFT vector and clusters SIFT clusters vector representing probability of each cluster, or “mixture” of clusters

  20. Martian training set = Test: Find Bob after one meeting ? = = Crash Course on Martian Identification Bob

  21. Training Data “same” “different”

  22. General Approach to Hyper-feature method • Carefully align objects • Develop a patch-based model of image differences. • Score match/mismatch based on patch differences.

  23. Three Models • Universal patch model: P(patchDistance|same) P(patchDistance|different) • Spatially dependent patch model: P(patchDistance |same,x,y) P(patchDistance |different,x,y) • Hyper-feature dependent model: • P(patchDistance |same,x,y,appearance) • P(patchDistance |different,x,y,appearance)

  24. Universal Patch Model A single P(dist | same) for all patches Different blue patches are evidence against a match!

  25. Spatial Patch Model P(dist|same,x1,y1) estimated separately from P(dist|same,x2,y2) Greatly increases discriminativeness of model.

  26. Hyper-Feature Patch Model Is the patch from a matching face going tomatch this patch?

  27. Hyper-Feature Patch Model Is the patch from a matching face going tomatch this patch? Probably yes

  28. Hyper-Feature Patch Model What about this patch?

  29. Hyper-Feature Patch Model What about this patch? Probably not.

  30. Ridiculous Errors from the World’s Best Unconstrained Face Recognition System

  31. Ridiculous Errors from the World’s Best Unconstrained Face Recognition System

  32. The New Mission: Estimate Higher Level Features

  33. The New Mission: Estimate Higher Level Features Can we guess pose?

  34. The New Mission: Estimate Higher Level Features Can we guess gender?

  35. The New Mission: Estimate Higher Level Features Can we guess degree of balding, beardedness, moustache?

  36. The New Mission: Estimate Higher Level Features Can we say that none of these individuals are the same person?

  37. What can we do with a good segmentation?

  38. CRF Segmentations

  39. CRF Segmentations

  40. Who’s This?

  41. Who’s This?

  42. Who’s This? from www.coolopticalillusions.com

  43. Thanks

More Related