Poster
in
Affinity Workshop: Women in Machine Learning

DEVELOPMENT OF A MODIFIED LIKELIHOOD RATIO MODEL FOR HANDWRITING IDENTIFICATION IN FORENSIC SCIENCE

Adeyinka Abiodun · Sesan Adeyemo · Adegboyega Adebayo


Abstract:

Likelihood Ratio (LR) is a means of quantifying the strength of evidence in a forensic investigation. Existing methods for estimating LR in handwriting identification employed nuisance parameters resulting into high rate of inconclusiveness and disagreement among forensic investigators. Currently, LR procedures rely on the choice of appropriate denominators that limit the repeatability and reproducibility of the estimated LR. Therefore, this study proposed developing a modified LR devoid of nuisance parameter and capable of generating consistent estimate. A total of 230 document writers were purposively selected to produce 10 paged true and disguised documents over a period of six months. Similar procedure was carried out to produce forged document for the corresponding true counterparts. Otsu’s method was used to preprocess the data, while Sobel edge detection was used to segment handwritings. The C-means was used to cluster handwriting into characters based on segmented words. Local binary pattern was used to extract features from the clustered characters and extracted features were fed into a Back Propagation Neural Network (BPNN) to learn the handwriting pattern. Exhaustive mapping algorithm with bias function was developed to replace the hitherto randomly selected denominator for the LR estimation. The derived handwriting pattern followed a normal distribution. The improved model had 0.0% inconclusive rate for KDE and LoR as against 22.2% inconclusiveness which is the minimum as reported in literature. The modified likelihood ratio produced consistent forensic estimates in terms of reproducibility and repeatability devoid of nuisance parameters. This modified likelihood ratio will give reliable estimates for forensics investigations.

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