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FACIAL COMPOSITES

When a crime has been committed and the identity of

the perpetrator is unknown, eyewitnesses are often

asked to attempt to create a likeness of the face of the

perpetrator. An eyewitness can do this by creating a

facial composite, either through the assistance of a

sketch artist or by using a mechanized composite sys-

tem. However, facial composites tend to be poor rep-

resentations of the intended face, even if it is a face

that is very familiar to the composite creator. This is

probably due to a mismatch between the way in which

people encode faces and the way in which they

attempt to recall faces when building a composite.

When facial composites were first introduced in

the criminal justice system, eyewitnesses would work

together with a sketch artist to create a likeness of the

intended face. Today, law enforcement agencies typi-

cally use mechanized composite production systems,

and computerized composite production systems are

used more than twice as often as noncomputerized

versions. The original mechanized composite produc-

tion systems, such as the Identi-Kit and Photo-Fit, are

composed of overlays of facial features (e.g., noses,

eyes, chins, hair) that can be combined to create a

face. Modern, computerized versions, such as E-fit,

Mac-a-Mug, and FACES, consist of features that can

be combined, and typically resized, in any order to

create a face. Currently, however, composite produc-

tion systems are being created that move away from

producing a face at the feature level and, instead,

focus on whole faces.

Many of the mechanized and computerized sys-

tems have attempted to increase the number of features

available from which a composite creator may choose,

the realism of the final product, and the user friendli-

ness of the interface. FACES, for example, has more

than 3,700 features, ranging from relatively prominent

features such as hair, eyes, and lips to detailed features

such as eye lines and mouth lines. The computerized

systems result in a fairly realistic product and can be

used after a minimal training session. However, even

when people view a face that has been created with a

composite system and attempt to re-create the face

using the same system, thereby ensuring that all the fea-

tures are available, they are still unable to create good

likenesses of the intended face. Furthermore, compos-

ite producers themselves are poor judges of how well

the composite that they have created matches the target

face. Even if a person who creates a composite rates the

composite’s similarity to the face that it is intended to

represent, this rating is not predictive of how others rate

the similarity of the composite to the target face.

Researchers have typically assessed people’s abil-

ity to create composites of faces through naming

tasks, matching tasks, and similarity-rating tasks.

Naming tasks show people a composite of someone

who should be familiar to them (e.g., a famous per-

son) and ask them to name the person the composite

is designed to depict. Matching tasks have people

choose which face the composite is designed to depict

from a larger set of faces. Similarity-rating tasks have

people rate the similarity between a composite and the

face it is designed to depict. In general, facial compos-

ites tend to be poor likenesses of the faces that they

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