Exact solutions to Bayesian and maximum likelihood problems in facial identification when population and error distributions are known

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Allen, Rory. 2008. Exact solutions to Bayesian and maximum likelihood problems in facial identification when population and error distributions are known. Forensic Science International(179), pp. 211-218. [Article]

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Text (Published in Forensic Science International 179 (2008) 211–218)
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Abstract or Description

The reliability of traditional photogrammetric identification techniques using a small number of facial landmarks has recently come in for
criticism. However, the transformation of parameters into a new face space in which the error distributions are orthogonal, yields a maximum
likelihood solution to the problem of identifying a photographed face from a small, known, population which, in a simulated example, raises the
success rate from 20% to 93%. A full transformation yielding simultaneously independent population and error distributions can be derived from
raw population and error data using a straightforward computer procedure. Such a transformation facilitates computations for the situation where a
single suspect is held in custody and the likelihood ratio of his being identical with a photograph is desired. It seems premature to condemn
photogrammetry until the more efficient data-analysis approach outlined in this paper has been applied and tested.

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