We describe a new test for unfamiliar face matching, the Glasgow Face Matching Test (GFMT). Viewers are shown pairs of faces, photographed in full-face view but with different cameras, and are asked to make same/ different judgments. The full version of the test comprises 168 face pairs, and we also describe a shortened version with 40 pairs. We provide normative data for these tests derived from large subject samples. We also describe associations between the GFMT and other tests of matching and memory. The new test correlates moderately with face memory but more strongly with object matching, a result that is consistent with previous research highlighting a link between object and face matching, specific to unfamiliar faces. The test is available free for scientific use.

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In this paper we describe how the microstructure of the Bruce & Young (1986) functional model of face recognition may be explored and extended using an interactive activation implementation. A simulation of the recognition of familiarity of individuals is developed which accounts for a range of published findings on the effects of semantic priming, repetition priming and distinctiveness. Finally, we offer some speculative predictions made by the model, and point to an empirical programme of research which it suggests.

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SummaryPeople are excellent at identifying faces familiar to them, even from very low quality images, but are bad at recognising, or even matching, faces that are unfamiliar. In this review we shall consider some of the factors which affect our abilities to match unfamiliar faces. Major differences in orientation (e.g. inversion) or greyscale information (e.g. negation) affect face processing dramatically, and such effects are informative about the nature of the representations derived from unfamiliar faces, suggesting that these are based on relatively low-level image descriptions. Consistent with this, even relatively minor differences in lighting and viewpoint create problems for human face matching, leading to potentially important problems over the use of images from security video images. The relationships between different parts of the face (its "configuration") are as important to the impression created of an upright face as local features themselves, suggesting further constraints on the representations derived from faces. The review then turns to consider what computer face recognition systems may contribute to understanding both the theory and the practical problems of face identification. Computer systems can be used as an aid to person identification, but also in an attempt to model human perceptual processes. There are many approaches to computer recognition of faces, including ones based on low-level image analysis of whole face images, which have potential as models of human performance. Some systems show significant correlations with human perceptions of the same faces, for example recognising distinctive faces more easily. In some circumstances, some systems may exceed human abilities on unfamiliar faces. Finally, we look to the future of work in this area, that will incorporate motion and three-dimensional shape information.