[5] The framework has also provided useful applications in the design of forensic techniques for eyewitness identification, such as facial composites and police lineups.
It is multidimensional, with each dimension categorised by certain facial features, some of which may be: face shape, hair colour and length, distance between the eyes, age and masculinity.
[3] Storing a face in a specific location within face-space involves the encoding of facial data into the dimensions of the framework.
[3] Whilst other factors like race, distinctiveness [1] and caricature effects [6] can circumstantially make encoding easier or more difficult.
Both models encode faces in a multidimensional psychological space and account for factors like race and inversion.
[6] The face-space framework is able to explain this finding as it assumes that faces are distributed normally across its dimensions.
[8][1] Norm-based face-space on the other hand explains the own-race bias as a consequence of distance from the ‘norm face’.
In particular, for both the fourth generation of facial composite systems[9] as well as fairer police line-ups for suspects with distinguishing features.
[10] Distinctive features replicated on multiple faces would mean they are nearer in face-space, and therefore perceived as more similar, according to the hybrid-similarity model.