Literature review (part 7)

"Perceptual Expertise Bridging Brain and Behavior Oxford Series in Visual Cognition" by Gauthier et al.: Gary suggested I check this book out, and I found some references which might be useful when talking about the cogsci aspect of external features. The following are quotations from the book:

"Adults and children alike find it easier to recognize an unfamiliar face based only on its external features than only on its internal features (Want, Pascalis, Coleman, & Blades, 2003)" pg 78

I already had a reference for this tidbit, but now I have another. Also, they might define "external feature", which would be useful.

"Thus, adults are more sensitive to featural changes than they are to spacing changes that cover most of the natural variability among faces in the real world but stay within normal limits (Farkas, 1981), a result leading to the conclusion that adults are adept at using featural differences in recognizing facial identity." pg 81

A featural change is something like swapping out the eyes on one face for the eyes on another. The result is adults are more sensitive to a "unit" of change to the features than to a unit of change to the configuration of the features. This is the same sort of thing to which a local descriptor like SIFT would be sensitive.

"Nonetheless, under some conditions features may not provide a reliable cue: when the face is seen from a new point of view, when the person poses a new facial expression, under poor lighting conditions, and after many years of aging. Under these conditions the appearance of individual features changes, and adults may need to rely on the spacing among facial features that comes from the bone structure of the face. It is not surprising then that adults are exquisitely sensitive to the spacing of facial features (Freire et al., 2000; Mondloch et al., 2002) and that limits in this sensitivity correspond to limits in their visual acuity (Ge, Luo, Nishiura, & Lee, 2003; Haig, 1984)." pg 81

The last bit here is potentially useful, as it suggests it is natural to use configuration in the case of low quality images. If this is true, it's not such a stretch to think it's useful to use larger, external features in the same case.

"Transferable Belief Model for hair mask segmentation" by Rousset et al.: Similar to previous work, in which local texture and color masks are used to determine seed pixels for an alpha matting algorithm. The difference here is the use of a transferable belief model, which is a theory of probability in which the user can explicitly encode lack of knowledge, leading to more conservative posterior beliefs. They update their texture and color detectors so they can output "uncertain" as well as "hair" or "not hair". Instead of doing an AND to get the hair seed, as they did in their previous paper, they use Dempster's rule of combination, an artifact of transferable belief models.

They run some experiments with frontal face shots of women (apparently the same dataset as before), comparing their old method with the current one, and the new one does better. This isn't surprising, as the old one had to make hard (binary) decisions for every pixel. Even a small number of mislabeled pixels could ruin the matte, so it's better to err conservative. The transferable belief model makes it easy to be conservative.