High-resolution facial feature saliency mapping

For recognition of a target there must be some form of comparison process between the image of that target and a stored representation of that target. In the case of faces there must be a very large number of such stored representations, yet human beings seem able to perform comparisons at phenomenal speed. It is possible that faces are memorized by fitting unusual features or combinations of features onto a bland prototypical face, and such a data-compression technique would help to explain our computational speed. If humans do indeed function in this fashion, it is necessary to ask just what are the features that distinguish one face from another, and also, what are the features that form the basic set of the prototypical face. The distributed apertures technique was further developed in an attempt to answer both questions. Four target faces, stored in an image-processing computer, were each divided up into 162 contiguous squares that could be displayed in their correct positions in any combination of 24 or fewer squares. Each observer was required to judge which of the four target faces was displayed during a 1 s presentation, and the proportion of correct responses for each individual square was computed. The resultant response distributions, displayed as brightness maps, give a vivid impression of the relative saliency of each feature square, both for the individual targets and for all of them combined. The results, while broadly confirming previous work, contain some very interesting and surprising details about the differences between the target faces.     

How faces differ--a new comparative technique

It can be argued that the process of recognizing faces progresses in two stages: first, the realisation that a perceived image contains patterns that may most reasonably be interpreted as forming a discrete face; second, correct and positive identification by noting the particular features that differentiate one face from all others. A novel technique which explored the latter process in the particular case of four different (male) faces is described. The experiment took the form of a four-alternatives forced-choice presentation of faces behind masks which contained a number of randomly positioned apertures. The percentage of correct responses for each separate aperture was then computed after a large number of 1 s presentations to four observers. This novel form of experiment suggested an equally novel form of pictorial data presentation that, literally, highlights the salient features of each individual face and thereby allows detailed intercomparison merely by inspection. Summing over all targets and observers reveals a strong preference for eyes and eyebrows, followed closely by the hairline above the temples. Next in order of preference comes the mouth and upper-lip area, followed by the lateral hairline beside each temple. Individual differences are strong, however, and the variations are such as to suggest that the uncritical application of generalised feature saliency lists is neither useful nor appropriate.