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Large-scale, Real-world facial recognition in movie trailers. Alan Wright Presentation 7. recap of last week. Cast selector. 3. Cast selector. Retrieves cast list from Rotten Tomatoes using their API. Ignore tracks we don’t want. Type custom names.
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Large-scale, Real-world facial recognition in movie trailers • Alan Wright • Presentation 7
Cast selector • Retrieves cast list from Rotten Tomatoes using their API. • Ignore tracks we don’t want. • Type custom names. • Allows two people to simultaneously label tracks and no labeling will be repeated. 4
Cast selector • All 2400+ tracks have now been labeled with the correct faces. • Faces not in PubFig were still labeled. • Easily label more tracks if new trailers are added. • If faces are added to PubFig, the labeling will not need to be redone. 5
Labeling results • 635 Unknown tracks • 712 PubFig tracks • 1113 labeled tracks (faces not in PubFig) • 4 ignored tracks.
Labeling results # of labels PubFig Ids
Labeling results • Katherine Heigl was labeled the most with 51 tracks. • Each PubFig face (in the trailers) has an average of 12 tracks.
Labeling Results • The most labeled face, not in PubFig, was Edward Norton with 53 tracks. • 218 faces were labeled, but not in PubFig. • Average of 5 tracks per face.
New pr curve • Accurate with labeled faces
How can we add more faces? • Look at the distribution of faces that aren’t in PubFig • Pick a threshold that will give us faces that appear often, and extend PubFig. • Note: We want a good threshold because the average number of tracks per person (not in PubFig) is 5.
# of labels Track distribution • Faces not in PubFig Face IDs
# of labels Threshold of 20 Track distribution • Faces not in PubFig Face IDs
New faces • Choosing a threshold of 20 or more tracks gives us 9 new people: • Edward Norton - 53 • Amanda Seyfried - 37 • Jason Bateman - 34 • Hilary Swank - 31 • Paul Rudd - 30 • Robert De Niro - 27 Leelee Sobiesk - 26 Dwayne Johnson - 24 Johnny Depp - 24
new faces • Downloaded images for these 9 people and added them to PubFig. (eye aligned, extracted features, etc)
new labeling distribution • 635 Unknown tracks • 998 Extended PubFig tracks • 827 labeled tracks (faces not in PubFig) • 4 ignored tracks.
What’s next? • Run over new supplemented data (Server will be up this afternoon) • Implement other voting methods: • Logarithmic pooling • Borda Count • Look at other ways to create a single confidence score for non-avg SRC and SVM methods • Experiment with different parameters: crop, pca dimensions, features, voting