A Signature Identification System With Principal Component Analysis and Stentiford Thinning Algorithms

Abstract

Several biometric security systems have been implemented. Biometric is the use of a person’s physiological or behavioural characteristics to identify the individual. An example of behavioural method of biometric is signature identification. Signature identification is the use of handwritten signature to identify a person. This paper attempt design and implement an algorithm for handwritten signature identification. The signature identification system consists of signature acquisition, preprocessing, features extraction and matching stages. Signature acquisition can be either online or offline (both were considered in this research work). Online signatures are obtained by signing on digital tablets while offline signatures are scanned (or snapped) into the system. Preprocessing stage of the system include turning the image to greyscale. The grey image is further converted to binary (black and white). The image is then thinned, using Stentiford thinning algorithm. Stentiford thinning algorithm in an iterative thinning method with a good thinned imaged output. The image is finally cropped to rid the image of unnecessary white spaces. For features extraction, principal component analysis is used. Principal Component Analysis is a good statistical tool for identifying pattern in data. Features extracted from each signature are stored as a template. After features extraction, the distance between signature templates are computed using Manhattan distance. If the distance exceeds a certain threshold, the test signature is rejected (otherwise it is accepted). The design system has a FAR of 4% and an FRR of 6% for offline signatures. A FAR of 2% and an FRR of 3% were obtained for online signatures