1. Feature based vs. holistic processing
Tanaka & Simonyi (2016), Sinha et al. (2006)
composite face effect
face inversion effect
whole-part effect

2. Feature based vs. holistic processing
composite face effect
face inversion effect
identical top halves seen as different when aligned with different bottom halves
when misaligned, top halves perceived as identical
whole-part effect
- (Kanade/features & PCA/holistic) question perceptual studies, not fully resolved, recognize faces based on feature vs. holistic process uses global, configural info about faces - perceptual phenomena used argue holistic, fuzzy notion in literature, examples of effects that people point to - composite face effect, left pair images - two top halves same or different? identical tops but when different bottoms aligned, appear different (illusion, really same), when bottoms misaligned, more likely make correct judgment, argue derive holistic rep face when parts aligned, prevents access detailed parts needed for comparison, demo famous people, small info e.g. eye, sufficient recognize famous person, mix woody allen/oprah winfrey disrupts recognition, interferes diagnostic capability individual features - inversion effect, more difficult recognize inverted faces, disparity between our ability recognize upright vs. inverted faces greater than disparity recognize other kinds objects, argue inversion disrupts holistic arrangement of collection of features, eyes, nose, mouth, inversion effect develops early childhood, prosopagnosics don’t exhibit (learning process over time does not occur, contributes to difficulty)
inversion disrupts recognition of faces more than other objects
prosopagnosics do not show effect
Identification of “studied” face is significantly better in whole vs. part condition

3. The power of averages, Burton et al. (2005)
PCA performance
average “shape”
- Burton et al, internal rep individuals based on average examples seen, avg famous people, how grab examples internet, ~frontal views, otherwise unconstrained (lighting, expression, time), not avg raw images - avg texture (captures typical variation brightness face), avg shape (shape manual step, generic grid, move key points to face location), (1) morph image to common geometric shape, do same each exemplar, then average (individuals not all look like Tony Blair, avg captures essence), but lost individual shape (large forehead), (2) avg locations key points on grid to get avg shape captured by mesh, (3) morph avg texture to grid avg face - quite effective recognize famous faces – train PCA based recog system with avgs, performs better recognize new individual images, improves more exemplars combined in avg – test commercial system FaceVACS, database ~ 31,000 photos famous faces, ~ 3,600 celebrities, online version can upload image & returns closest matching photo from DB (black box) – 460 individual images of famous people 54% correct, with average faces as input, reaches 100% correct, then computed average using 46% wrong/80% correct - why? what doing with average that’s advantageous? avg factors e.g. lightness variations, expression, not diagnostic identity - perceptual experiments recognize famous faces, avg reaction times faster, less error ID avg faces vs. exemplars, how to automate construction of representation? how to evolve over time as become more familiar?
average “texture”

4. Human recognition of average faces
Burton et al. (2005)
Performance: texture + shape images
Performance: shape-free images