Forensic Analysis of Offline Signatures Using Multilayer Perceptron and Random Forest

Abstract

Forensic applications having great importance in the digital era, for the investigation of different types of crimes. The forensic analysis includes Deoxyribonucleic Acid (DNA) test, crime scene video and images,, forged documents analysis, computer-based data recovery, fingerprint identifications, handwritten signature verification and facial recognition. The signatures are divided into two types i.e. genuine and forgery. The forgery signature can lead to the huge amount of financial losses and create other legal issues as well. The process of forensic investigation for the verification of genune signature and detection of forgery signature in law related departements has been manula and the same can be automated using digital image processing techniques, and automated forensic signature verificatiob applications. The signatures represent any person's authority to the forged signature may also be used in a crime. Research has been done to automate the forensic investigation process, but due to the internal verification of signatures, the automation of signature verification still remains a challenging problem for researchers. In this paper, we have further extended previous research carried out in [1-2] and proposed a Forensic signature verification model based on two classifiers i.e. Multi-layer Perception (MLP) and Random Forest for the classification of genuine and forgery signatures.