Abstract
A key challenge in human and computer face recognition is to differentiate information that is diagnostic for
identity from other sources of image variation. Here, we used a combined computational and behavioural
approach to reveal critical image dimensions for face recognition. Behavioural data were collected using a
sorting and matching task with unfamiliar faces and a recognition task with familiar faces. Principal components
analysis was used to reveal the dimensions across which the shape and texture of faces in these tasks varied. We
then asked which image dimensions were able to predict behavioural performance across these tasks. We found
that the ability to predict behavioural responses in the unfamiliar face tasks increased when the early PCA dimensions (i.e. those accounting for most variance) of shape and texture were removed from the analysis. Image
similarity also predicted the output of a computer model of face recognition, but again only when the early image
dimensions were removed from the analysis. Finally, we found that recognition of familiar faces increased when
the early image dimensions were removed, decreased when intermediate dimensions were removed, but then
returned to baseline recognition when only later dimensions were removed. Together, these findings suggest that
early image dimensions reflect ambient changes, such as changes in viewpoint or lighting, that do not contribute
to face recognition. However, there is a narrow band of image dimensions for shape and texture that are critical
for the recognition of identity in humans and computer models of face recognition.
Original language | English |
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Number of pages | 0 |
Journal | Vision Research |
Volume | 212 |
Issue number | 0 |
Early online date | 30 Jul 2023 |
DOIs | |
Publication status | E-pub ahead of print - 30 Jul 2023 |