1.1.4.1. Famous Faces
Many studies use famous faces as stimuli. These are sometimes "iconic" photographs, and probably test pictorial recognition (of the image) rather than face recognition (Carbon, 2008; Burton, 2013). Familiarity with these faces is also generally limited to two-dimensional images (e.g. films, television or magazines), which is not the way we typically become familiar with people in real life. Different images of an actor can also differ from each other more than images of a person we know personally, as actors are employed to portray different characters with different appearances (and accents). This is not typical of personally-familiar face recognition in the real world.
Thus, recognising the faces of famous people is probably different from recognising the faces of people that are known personally.
1.1.4.2. Personally-Familiar Faces
Some studies use personally-familiar faces, as exposure to them is extensive, often spanning years, with multiple views from different angles, in different lighting, with hairstyle and weight changes, and changes associated with health, ageing etc. People also get to know personally-familiar faces as they move in multi-dimensional space. As result, people form stable face representations (mental abstractions) of personally-familiar faces (Hancock, Bruce, & Burton, 2000). These are sensitive to differences between faces of different individuals (between-face differences), but can also accommodate fluctuations in a person’s appearance that occur due to different lighting, viewpoints etc. (within-face differences) (Johnston & Edmonds, 2009). These representations are far more robust than those of experimentally-learnt faces. However, using personally-familiar faces can be problematic in an experiment, as faces that are highly familiar to one person may well be unfamiliar to another.
1.1.4.3. Experimentally-familiar Faces
Experience with faces that are familiarised in experimental paradigms cannot produce the robust representations associated with personally-familiar or famous faces (Tong & Nakayama, 1999). However, tests using familiarised faces suggest that relatively little exposure to new faces can be sufficient for them to appear familiar. For example, Clutterbuck and Johnston (2005) found that although familiar faces were matched more quickly than new or newly-learnt faces, newly-learnt faces were matched more quickly than completely new faces. These studies therefore suggest that using familiarised faces
can be useful for understanding how quickly faces become familiar and how exposure can mediate learning. Familiarisation studies also have the advantage of reducing lexical, episodic or semantic memory associated with personally-familiar or famous faces. This makes it easier to draw conclusions about e.g. how much exposure is necessary for the early stages of face learning.
1.1.4.4. Own Faces
Own face images are also sometimes used in experiments. Previous research suggests that they require more effort to process and may be treated as more unfamiliar than other familiar faces (Brédart, 2003) because people generally only see themselves in the mirror (and faces are not entirely symmetrical). However, nowadays, many people see frequent images of their own face on mobile phones or on social media (see Senft & Baym, 2015, for a review), so they should have an intimate knowledge of their own (veridical) face from multiple angles and their face seen in the mirror (mirror-reversed). In short, nowadays, people should be more familiar with their own face than they are with any other face. Research also suggests that self-relevant stimuli are important, and this extends to own face images (Kircher et al., 2001; Tacikowski & Nowicka, 2010). This interest in one’s own image is demonstrated in the selfie phenomenon, where doctored images are shared as idealised representations of the self (Murray, 2015). Therefore, own face images now provide stimuli that should be both highly familiar in veridical and mirror-reversed format, and particularly engaging to the person viewing them. Thus, the use of own face images can be problematic as the rise in technology has dramatically changed the ways that own face images are accessed. The results are also difficult to generalise as own face images are different for each participant.
Research has revealed that familiar faces are easy to recognise, while it is much more difficult to recognise a face that has only been seen briefly before. One explanation for this is that representations of familiar faces can be applied flexibly to familiar faces seen in different conditions, while representations of unfamiliar faces rely upon on poor or fragmented information that make them harder to recognise when seen in different conditions (Hancock et al., 2000). This explains why familiar face recognition is good even when image quality is poor, and why unfamiliar face recognition is poor even when image quality is good. Burton, Jenkins, and Schweinberger (2011) suggest that familiar face processing is based on abstract structural codes: when a familiar face is seen, its characteristics can be matched to its stored representations, even when the face is seen under novel conditions. However, unfamiliar face processing is based on pictorial codes that are less flexible and make recognition in different conditions more difficult. Zimmermann & Eimer's (2013) research supports this distinction, as they found that familiar face recognition is possible from multiple views, while unfamiliar face recognition is more view-dependent. These findings all suggest that poor unfamiliar face recognition might be related to limited and inflexible information about the faces.
Research into the role of movement in face processing also supports this view. Knight & Johnston (1997) found that watching videos of moving faces aided recognition. They suggest that this is in part due to providing three-dimensional and characteristic information. Lander & Bruce (2000) found a similar advantage for famous faces, and Lander & Bruce (2003) found that motion improved unfamiliar face learning. They suggest that this is related to increased attention to socially-important facial movement. Xiao, Quinn, Ge, & Lee (2012) concluded that motion affects featural rather than holistic processing, suggesting that featural processing is important to face recognition. Overall, it is likely that movement helps with face recognition, as it provides additional
information about the structure and characteristics of the face seen from multiple viewpoints, allowing for the development of dynamic representations (Pilz, Bülthoff, & Vuong, 2009).
Thus, the distinction between familiar and unfamiliar faces is not simple. Unfamiliar faces can refer to those that are entirely novel or those that have been seen briefly before, yet experimental paradigms often compare faces that have only been seen briefly before to completely novel faces. As for faces that are generally agreed to be familiar, there are also differences: famous faces are generally known two-dimensionally, but personally-familiar faces are experienced contextually and three-dimensionally. Finally, own face recognition is difficult to categorise, as recent advances in technology mean that we have more familiarity with our own faces than ever before. Therefore, the question about how much experience with a face gives rise to a sense of familiarity has not been answered definitively. One theory suggests that faces lie on a continuum of familiarity (Rhodes, 1985): recognition becomes easier as representations become more robust and flexible, until faces can be recognised even from a novel or poor view. This is because increasingly robust (abstract) representations allow people to account for within- face variability that is a consequence of lighting or viewpoint etc., and to separate this from between-face variability that is a consequence of different faces.