Handwritten signature verification by independent component analysis

Abstract

This study explores a method that learns about the image structure directly from the image ensemble in contrast to other methods where the relevant structure is determined in advance and extracted using hand-engineered techniques. In tasks involving the analysis of image ensembles, important information is often found in the higher-order relationships among the image pixels. Independent Component Analysis (ICA) is a method that learns high-order dependencies found in the input. ICA has been extensively used in several applications but its potential for the unsupervised extraction of features for handwritten signature verification has not been explored. This study investigates the suitability of features extracted from images of handwritten signatures using the unsupervised method of ICA to successfully discriminate between different classes of signatures.