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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 • 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 • 827 labeled tracks (faces not in PubFig) • 4 ignored tracks. New Faces
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
Track breakdown • Known: 1310 - 36% • Labeled Distractor: 1236 - 34% • Unknown: 1039 - 28% • Ignored: 13 - 0.36%
Track breakdown • Known: 1310 - 36.41% • Distractor: 2275 - 63.23% • Ignored: 13 - 0.36%
lda OR PCA? 32 dim 64 dim 128 dim Not enough classes for LDA to work with higher dimensions
L2 and L2_AVG • Need to determine whether something in the method isn’t preforming correctly or it preforms poorly on dataset
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)
What’s next? • Test higher dimensions of PCA to choose final. • Continue to work with L2 and L2_AVG. • Test on higher dimensions.