Harvard University, Cambridge, MA., USA
Face Recognition plus Composite Creation
- Eigenfaces (Turk & Pentland 1991)
- Uses PCA to compress images to a low dimensional space of small set of basis vectors called eigenfaces.
- Location in eigenface-space determines the distance between images.
- Distance from a query image can be used to specify a sort order on a database.
- Composites
- How well does the eigenface metric correlate with users' assessments of facial similarity?
- Given whatever level of correlation there is between eigenfaces and human users, what search strategies make the best use of it?
- Are the composites helpful?
- Uses eigenface engine and 4500 image database from Photobook (Pentland, Picard, Sclaroff - 1994).
- Queries are either database faces or composites.
- Composites are constructed by recombining parts from images in the database.
- Interim composites may be used for retrieval and interim retrieval results may likewise be used to update an evolving composite.
View 100 Random Database Faces
System Generates 10 Random Composites From User's Choices.
User Produces a Composite Via Manual Editing
- image score = number of image inspections required to find target if that image is used as a query.
- strategy score = number of image inspections required to find target using that strategy.
- Determine image scores for each of users':
- Top five database choices
- Random composite choice
- Final edited composite
- Which strategies elicit the best average scores over all subjects?
Best and Worst Case Expected Strategy Scores
- Worst Case : sequential search on 4500 images --expected strategy score is 2250.
- Expected image score of closest of N random selections is ~(DatabaseSize/N).
- 4500/100 = 45, so expected score of closest image in random set of 100 is 45.
- Best Case: expected strategy score is 100+45 = 145.
Eigenface Best vs. Users' best
