The segmental structure of faces and its use in gender recognition

What is the relationship between object segmentation and recognition? First, we develop a feature segmentation method that parses faces into features and, in doing so, attempts to approximate human performance. This segmentation-based approach allows us to build featural representations that make explicit the part-whole structure of faces and removes a priori assumptions from the equation of how objects come to be divided into features. Second, we examine the utility and the psychological plausibility of this representation by applying it to the task of facial gender recognition. Featural information from the segmentation process is shown to support relatively high accuracy levels with automatic gender categorization. The diagnosticity of featural information, in particular color information as encoded by the three perceptual color channels, is traced to the different patterns of feature contrast across Caucasian male and female faces. Results with human recognition suggest the visual system can exploit this information, however, there are open questions regarding the contribution of color information independent of luminance. More generally, our approach allows us to clarify and extend the notion of "configural" representations to multiple cues (i.e., not only shape) by considering relations between features independent of cue domain.