Signature Verification Approach using Fusion of Hybrid Texture Features

In this paper, a writer-dependent signature verification method is proposed. Two different types of texture features, namely Wavelet and Local Quantized Patterns (LQP) features, are employed to extract two kinds of transform and statistical based information from signature images. For each writer two separate one-class support vector machines (SVMs) corresponding to each set of LQP and Wavelet features are trained to obtain two different authenticity scores for a given signature. Finally, a score level classifier fusion method is used to integrate the scores obtained from the two one-class SVMs to achieve the verification score. In the proposed method only genuine signatures are used to train the one-class SVMs. The proposed signature verification method has been tested using four different publicly available datasets and the results demonstrate the generality of the proposed method. The proposed system outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio

Hindi off-line signature verification

Handwritten Signatures are one of the widely used biometrics for document authentication as well as human authorization. The purpose of this paper is to present an offline signature verification system involving Hindi signatures. Signature verification is a process by which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Despite of substantial research in the field of signature verification involving Western signatures, very little attention has been dedicated to non-Western signatures such as Chinese, Japanese, Arabic, Persian etc. In this paper, the performance of an off-line signature verification system involving Hindi signatures, whose style is distinct from Western scripts, has been investigated. The gradient and Zernike moment features were employed and Support Vector Machines (SVMs) were considered for verification. To the best of the authors' knowledge, Hindi signatures have never been used for the task of signature verification and this is the first report of using Hindi signatures in this area. The Hindi signature database employed for experimentation consisted of 840 (35×24) genuine signatures and 1050 (35×30) forgeries. An encouraging accuracy of 7.42% FRR and 4.28% FAR were obtained following experimentation when the gradient features were employed. © 2012 IEEE

A two-stage approach for English and Hindi off-line signature verification

The purpose of this paper is to present an empirical contribution towards the understanding of multi-script off-line signature identification and verification using a novel method involving off-line Hindi (Devnagari) and English signatures. The main aim of this approach is to demonstrate the significant advantage of the use of signature script identification in a multi-script signature verification environment. In the 1st stage of the proposed signature verification technique a script identification technique is employed to know whether a signature is written in Hindi or English. In the second stage, a verification approach was explored separately for English signatures and Hindi signatures based on the script identification result. Different features like gradient feature, water reservoir feature, loop feature, aspect ratio etc. were employed, and Support Vector Machines (SVMs) were considered in our scheme. To get the comparative idea, multi-script signature verification results on the joint Hindi and English dataset without using any script identification technique is also computed. From the experiment results it is noted that we are able to reduce average error rate 4.81% more when script identification method is employed. © 2013 Springer-Verlag