Offline Handwritten Signature Verification Based on Local Ridges Features and Haar Wavelet Transform

Mela G Abdul-Haleem

doi:10.24996/ijs.2022.63.2.38

Abstract

Multiple applications use offline handwritten signatures for human verification. This fact increases the need for building a computerized system for signature recognition and verification schemes to ensure the highest possible level of security from counterfeit signatures. This research is devoted to developing a system for offline signature verification based on a combination of local ridge features and other features obtained from applying two-level Haar wavelet transform. The proposed system involves many preprocessing steps that include a group of image processing techniques (including: many enhancement techniques, region of interest allocation, converting to a binary image, and Thinning). In feature extraction and analysis stages, a combination of local ridge features and other features obtained from the details of Haar wavelet subbands are extracted. Each wavelet sub-band image is fragmented into blocks with overlap and then the local features and wavelet energies are extracted from each block. Experiments were performed using a database of 600 signature prints collected from 100 persons, (i.e., 6 samples per person). The recognition accuracy of the system was the optimum (100%) using two decomposition levels. For verification purposes, the False Reject Rate for the system was (0.025%) while False Acceptable Rate was (0.03%) respectively.

Highlights

The input of an offline system is a static signature image mostly scanned at a high resolution
Because of the differences in the input, preprocessing, feature extraction and recognition techniques used, online and offline systems have a great difference in their approaches [3,4]
During the feature extraction phase, a group of local ridge features in association with features obtained by applying two-levels of Haar Wavelet Transform (HWT) are used and examined

Summary

Introduction

The verification of people by biometrics is an important technology in our life. With all the behavioral biometric features such as keystroke, voice, and gait, a signature is the most utilized feature to reduce forgeries. An automatic system for signature verification can be either online or offline, according to the method used to acquire the signature. The most important applications of these systems are the verification of signatures on bank checks and vouchers. Because of the differences in the input, preprocessing, feature extraction and recognition techniques used, online and offline systems have a great difference in their approaches [3,4]. This work aims to find the best set of features and defines a new promising approach in handwritten signature verification systems that extracts signature features using Haar wavelet technique and a set of local ridges features. The results of the feature extraction and investigation of the recognition accuracy and the evaluation of the performance of the proposed model are explained in the experiment and results section. The main conclusions and a list of recommendations for future works are presented in the last section

Objectives

Results

Conclusion