Quantifying the effect of viewpoint changes on sensitivity to face identity

Healthy aging impairs face discrimination ability

Face images enable individual identities to be discriminated from one another. We aimed to quantify age-related changes in different aspects of face identity discrimination. Face discrimination sensitivity was measured with a memory-free “odd-one-out” task. Five age groups (N = 15) of healthy adults with normal vision were tested: 20, 50-59, 60-69, 70-79, and 80-89. Sensitivity was measured for full-face images (all features visible), external features (head-shape, hairline), internal features (nose, mouth, eyes, and eyebrows) and closed-contour shapes (control object). Sensitivity to full-faces continuously declined by approximately 13% per decade, after 50 years of age. When age-related differences in visual acuity were controlled, the effect of age on face discrimination sensitivity remained. Sensitivity to face features also deteriorated with age. Although the effect for external features was similar to full-faces, the rate of decline was considerably steeper (approximately 3.7 times) for internal, relative to external, features. In contrast, there was no effect of age on sensitivity to shapes. All age groups demonstrated the same overall pattern of sensitivity to different types of face information. Healthy aging was associated with a continuous decline in sensitivity to both full-faces and face features, although encoding of internal features was disproportionately impaired. This age-related deficit was independent of differences in low-level vision. That sensitivity to shapes was unaffected by age suggests these results cannot be explained by general cognitive decline or lower-level visual deficits. Instead, healthy aging is associated with a specific decline in the mechanisms that underlie face discrimination.

The sizable difficulty in matching unfamiliar faces differing only moderately in orientation in depth is a function of image dissimilarity

Attempting to match unfamiliar, highly similar faces at moderate differences in orientation in depth is surprisingly difficult. No neurocomputational account of these costs that addressed the representation of faces by which a face-similarity metric can be derived has been offered. A metric specifying the similarity of the to-be-distinguished faces is required as the rotation costs will be a function of the difficulty in distinguishing the faces. Consequently, rotation costs have typically been described in terms of angle of disparity, rather than the dissimilarity of the faces produced by the rotation. We assessed the effects of orientation disparity in a match-to-sample paradigm of a simultaneous presentation of a triangular display of three faces. Two lower test faces, a matching face and a foil, were always at the same orientation and differed by 0° to 20° from the sample on top. The similarity of the images was scaled by a model based on simple cell tuning, modeled as Gabor wavelets, that correlates almost perfectly with psychophysical similarity. Two measures of face similarity, with approximately additive effects on reaction times, accounted for matching performance: a) the decrease in similarity between the images of the matching and sample faces produced by increases in their orientation disparity, and b) the similarity between the matching face and the selection of a particular foil. The 20° orientation disparity was sufficient to yield a sizeable 301 msec increase in reaction time. An implication of the results is that the activity in V1 produced by viewing a face is fed forward to areas responsible for the individuation of that face.

Average faces: How does the averaging process change faces physically and perceptually?

Average faces have been used frequently in face recognition studies, either as a theoretical concept (e.g., face norm) or as a tool to manipulate facial attributes (e.g., modifying identity strength). Nonetheless, how the face averaging process- the creation of average faces using an increasing number of faces -changes the resulting averaged faces and our ability to differentiate between them remains to be elucidated. Here we addressed these questions by combining 3D-face averaging, eye-movement tracking, and the computation of image-based face similarity. Participants judged whether two average faces showed the same person while we systematically increased their average level (i.e., number of faces being averaged). Our results showed, with increasing averaging, both a nonlinear increase of the computational similarity between the resulting average faces and a nonlinear decrease of face discrimination performance. Participants' performance dropped from near-ceiling level when two different faces had been averaged together to chance level when 80 faces were mixed. We also found a nonlinear relationship between face similarity and face discrimination performance, which was fitted nicely with an exponential function. Furthermore, when the comparison task became more challenging, participants performed more fixations onto the faces. Nonetheless, the distribution of fixations across facial features (eyes, nose, mouth, and the center area of a face) remained unchanged. These results not only set new constraints on the theoretical characterization of the average face and its role in establishing face norms but also offer practical guidance for creating approximated face norms to manipulate face identity.