Online handwritten signature verification using neural network classifier based on principal component analysis.

Vahab Iranmanesh,Wan Azizun Wan Adnan,Sharifah Mumtazah Syed Ahmad,Fahad Layth Malallah,Salman Yussof,Olasimbo Ayodeji Arigbabu

doi:10.1155/2014/381469

Abstract

One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%.

Highlights

Biometrics can be literally described as human biological characteristics that can be used for recognition [1]
This study measured the performance of the proposed online signature verification (OSV) system based on 50 selected features after implementing principal component analysis (PCA) on the signature to represent it in the verification system
The average of false acceptance rate (FAR) and false rejection rate (FRR) is defined as a misclassified rate, with 6.9% that is approximately close to an equal error rate (EER)

Summary

Introduction

Biometrics can be literally described as human biological characteristics that can be used for recognition [1]. Biometric recognition systems are normally developed for two main purposes, which are identification and verification. A biometric system can be modeled based on either physical or behavioral traits of individuals [1]. Physical traits such as face, fingerprint, and iris are very unique to every individual and are stable over an extended period of time [1]. Biometric systems, which are based on these traits, are usually accurate and reliable enough for identification purposes that involve one to many comparisons [1, 4]

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Discussion

Conclusion