Mobile Based Offline Handwritten Signature Forgery Identification using Convolutional Neural Network

A signature is one of the official biometric identification media that has special characteristics that are difficult to distinguish just by comparing with existing signatures. This causes irresponsible people to use signatures as objects of forgery. In addition, handwritten signature identification is a challenge because it has less information when compared to digital signatures. This research proposes a new approach to identify handwritten signature forgeries using a convolutional neural network (CNN) using mobile-based. Two model architectures are compared with the same testing scenario. The first architecture is a simplification of the Siamese convolutional network namely adjusted CNN. The second architecture is a standard CNN with 6 convolution layers which in this study is called general CNN. CNN is used for both feature extraction and classification processes. The dataset is built with a limited number of individuals consisting of genuine and forged signatures. Based on the experiments, the accuracy of each network reached 88.89% for adjusted CNN and 84.45% for general CNN. Adjusted CNN then embedded in a mobile-based application to identify the signature directly. The application that is built makes the process of identifying signatures more ease.