title: Exact solutions to Bayesian and maximum likelihood problems in facial identification when population and error distributions are known.
abstract: 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.
authors.affiliation: Department of Psychology, Goldsmiths College, New Cross, London SE14 6NW, UK. r.allen@gold.ac.uk
authors.forename: Rory J
authors.initials: RJ
authors.lastname: Allen
mesh_terms: D001499:Bayes Theorem; D001699:Biometry; D003415:Crime; D005145:Face; D005554:Forensic Medicine; D006801:Humans; D016013:Likelihood Functions; D010780:Photogrammetry; D010781:Photography; D014743:Videotape Recording