facegen_modeller_35_full_version_free__akmtd v1.0.0

Facegen Modeller 35 Full Version Free Download

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the present study investigated whether cg faces of the type created by the facegen program are processed like real face photographs in terms of their ability to tap into face expertise. the results of two experiments demonstrated that cg faces are not capable of fully engaging face expertise. experiment 1 found that the well-established inversion and other-race effect for face recognition was reduced for cg faces compared to real photographs. experiment 2 found that the reduction in the inversion effect for own-race compared to other-race cg faces was absent, indicating that cg faces do not engage expertise in the same way as real photographs. together these results suggest that the addition of realistic surface detail to cg faces created by facegen may be crucial for the full demonstration of face expertise. we therefore recommend that caution is exercised when interpreting findings using cg faces and that their use be limited to tasks requiring a relatively impoverished level of expertise. finally, we discuss the implications of these results in the context of the emerging field of face computing.


we also examined whether the ore is restricted to one type of expertise. a recent study suggests that expertise is domain specific [ 7 ] and that expertise with faces might differ from expertise with non-face objects. to investigate this possibility, we compared cg faces to three object categories: real faces, manmade objects, and non-face objects. for all the object categories, the svm successfully detected faces (figure 3e, f, g). thus, the ore is not limited to expertise with faces, but is more widespread. it may be particularly relevant to expertise with faces that some features of real faces, such as the eyes, are not sufficiently present in cg faces to enable the svm to identify faces. this is because cg faces have a smooth, featureless surface, whereas real face images typically contain rich detail and texture. the lack of face-specific information in cg faces could therefore reduce their ability to engage face processing regions of the brain. while the svm appears to be able to distinguish cg faces from manmade objects, this difference was not significant, and the reason for this difference is unknown. in both cases, the performance of the svm was comparable with the performance of the previous human experiment 77  ( supplementary fig. 12b, c ). 84d34552a1