Forensic Writer Identification Using Allographic Features

Abstract

Questioned document examination is extensively used by forensic specialists for criminal identification. This paper presents a writer recognition system based on allographic features operating in identification mode (one-to-many). It works at the level of isolated characters, considering that each writer uses a reduced number of shapes for each one. Individual characters of a writer are manually segmented and labeled by an expert as pertaining to one of 62 alphanumeric classes (10 numbers and 52 letters, including lowercase and uppercase letters), being the particular setup used by the forensic laboratory participating in this work. A codebook of shapes is then generated by clustering and the probability distribution function of allograph usage is the discriminative feature used for recognition. Results obtained on a database of 30 writers from real forensic documents show that the character class information given by the manual analysis provides a valuable source of improvement, justifying the proposed approach. We also evaluate the selection of different alphanumeric channels, showing a dependence between the size of the hit list and the number of channels needed for optimal performance.