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large-scale, real-world facial recognition in movie trailers

large-scale, real-world facial recognition in movie trailers. Alan Wright Presentation 8. quick Recap. Last Few Weeks: Added 9 new faces to the dictionary to get more tracks. Preliminary Curves. quick recap. 635 Unknown tracks 998 Extended PubFig tracks

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large-scale, real-world facial recognition in movie trailers

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  1. large-scale, real-world facial recognition in movie trailers • Alan Wright • Presentation 8

  2. quick Recap • Last Few Weeks: • Added 9 new faces to the dictionary to get more tracks. • Preliminary Curves

  3. quick recap • 635 Unknown tracks • 998 Extended PubFig tracks • 827 labeled tracks (faces not in PubFig) • 4 ignored tracks. New Faces

  4. Dataset • Added one final face to dictionary. • 210 final faces in dictionary (200 Pubfig + 10) • Total of 108 videos (added videos with our extra 10 faces) • 3585 tracks

  5. Track breakdown • Known: 1310 - 36% • Labeled Distractor: 1236 - 34% • Unknown: 1039 - 28% • Ignored: 13 - 0.36%

  6. Track breakdown • Known: 1310 - 36.41% • Distractor: 2275 - 63.23% • Ignored: 13 - 0.36%

  7. lda OR PCA? 32 dim 64 dim 128 dim Not enough classes for LDA to work with higher dimensions

  8. PCA dimensions

  9. pca 1024 precision recall

  10. PCA time

  11. L2 and L2_AVG • Need to determine whether something in the method isn’t preforming correctly or it preforms poorly on dataset

  12. Additional dataset • YouTube Celebrity Dataset • “Face Tracking and Recognition with Visual Constraints in Real-World Videos” • Project Page • Allows us to test and verify on an additional dataset. • We can use our dictionary (PubFig + 10)

  13. What’s next? • Test higher dimensions of PCA to choose final. • Continue to work with L2 and L2_AVG. • Test on higher dimensions.

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