Abstract
Recent psychophysical evidence indicates that the vertical arrangement of horizontal information is particularly important for encoding facial identity. In this paper we extend this notion to examine the role that information at different (particularly cardinal) orientations might play in a number of established phenomena each a behavioral “signature” of face processing. In particular we consider (a) the face inversion effect (FIE), (b) the facial identity after-effect, (c) face-matching across viewpoint, and (d) interactive, so-called holistic, processing of face parts. We report that filtering faces to remove all but the horizontal information largely preserves these effects but conversely, retaining vertical information generally diminishes or abolishes them. We conclude that preferential processing of horizontal information is a central feature of human face processing that supports many of the behavioral signatures of this critical visual operation.
Highlights
Facial information is of paramount significance to social primates such as humans
Similar to previous report for the naming of famous faces (Dakin and Watt, 2009), the present findings indicate that the ability to discriminate between unfamiliar faces is best supported by horizontal information
The loss of horizontal processing advantage with face inversion indicates that the better efficiency for processing horizontal information in upright faces does not merely derive from stimulus orientation structure, but arises as a consequence of the interactions between stimulus structure and observer
Summary
Facial information is of paramount significance to social primates such as humans. we have developed visual mechanisms which support the recognition of thousands of individuals based only on facial information, while resisting the sometimes drastic changes in appearance that arise from changes in distance, lighting, or viewpoint. From the point of view of engineering, a number of automated face recognition algorithms make use of principal component analysis (or similar techniques for reducing data-dimensionality) deriving basis images from a set of sample faces, such that any face can be decomposed into a weighted-sum of eigenfaces (Sirovich and Kirby, 1987; Turk and Pentland, 1991). Such approaches enjoy varying levels of success but are limited by the fact that they operate in a space that is determined by the representation of the raw data (i.e., lists of pixel values). For example it has recently been shown that the structure of many of the most significant eigenfaces (i.e., those that can capture most of the variation between individual faces) serves to capture gross image structure due to variation in lighting (Sirovich and Meytlis, 2009)
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