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.

No abstract

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.

No abstract

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.

People can be inaccurate at matching unfamiliar faces shown in high-quality video images, even when viewpoint and facial expressions are closely matched. However, identification of highly familiar faces appears good, even when video quality is poor. Experiment 1 reported a direct comparison between familiar and unfamiliar faces. Participants who were personally familiar with target items appearing on video were highly accurate at a verification task. Unfamiliar participants doing the same task performed very inaccurately. Familiarity affected discriminability, but not bias. Experiments 2 and 3 showed that brief periods of familiarization have little beneficial effect unless "deep" or "social" processing is encouraged. The results show that video evidence can be used effectively as a probe to identity when the faces shown are highly familiar to observers, but caution should be used where images of unfamiliar people are being compared.

Photo-ID is widely used in security settings, despite research showing that viewers find it very difficult to match unfamiliar faces. Here we test participants with specialist experience and training in the task: passport-issuing officers. First, we ask officers to compare photos to live ID-card bearers, and observe high error rates, including 14% false acceptance of ‘fraudulent’ photos. Second, we compare passport officers with a set of student participants, and find equally poor levels of accuracy in both groups. Finally, we observe that passport officers show no performance advantage over the general population on a standardised face-matching task. Across all tasks, we observe very large individual differences: while average performance of passport staff was poor, some officers performed very accurately – though this was not related to length of experience or training. We propose that improvements in security could be made by emphasising personnel selection.

We are able to recognise familiar faces easily across large variations in image quality, though our ability to match unfamiliar faces is strikingly poor. Here we ask how the representation of a face changes as we become familiar with it. We use a simple imageaveraging technique to derive abstract representations of known faces. Using Principal Components Analysis, we show that computational systems based on these averages consistently outperform systems based on collections of instances. Furthermore, the quality of the average improves as more images are used to derive it. These simulations are carried out with famous faces, over which we had no control of superficial image characteristics. We then present data from three experiments demonstrating that image averaging can also improve recognition by human observers. Finally, we describe how PCA on image averages appears to preserve identity-specific face information, while eliminating non-diagnostic pictorial information. We therefore suggest that this is a good candidate for a robust face representation.2 Robust representations for face recognition: the power of averages