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. 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 Dataset

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. TRACK BREAKDOWN How can we even out the distribution? Remove top 8 videos with unknowns (7%) Known: 39.1% Unknown: 61.2%

8. lda OR PCA? 32 dim64 dim128 dim Not enough classes for LDA to work with higher dimensions

9. PCA dimensions

10. pca 1024 precision recall

11. PCA time

12. recap

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

14. Additional dataset YouTube Celebrity Dataset “Face Tracking and Recognition with Visual Constraints in Real-World Videos”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)

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