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

• 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

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 dimNot enough classes for LDA to work with higher

dimensions

PCA dimensions

pca 1024 precision recall

PCA time

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.