504 research outputs found
Automatic Handwritten Signature Verification System for Australian Passports
We present an automatic handwritten signature verification system to prevent identity fraud by verifying the authenticity of signatures on Australian passports. In this work, fuzzy modeling has been employed for developing a robust recognition system. The knowledge base consists of unique angle features extracted using the box method. These features are fuzzified by an exponential membership function, consisting of two structural parameters which have been devised to track even the minutest variations in a person's signature. The membership functions in turn constitute the weights in the Takagi-Sugeno (TS) model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. The efficacy of the proposed system has been tested on a large database of over 1200 signature images obtained from 40 volunteers achieving a recognition rate of more than 99%
Feature Representation for Online Signature Verification
Biometrics systems have been used in a wide range of applications and have
improved people authentication. Signature verification is one of the most
common biometric methods with techniques that employ various specifications of
a signature. Recently, deep learning has achieved great success in many fields,
such as image, sounds and text processing. In this paper, deep learning method
has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information
Forensics and Securit
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
A Fuzzy Logic Based Novel Signature Verification System on Bank Cheque with Fractal Dimensions and Connected Components
Signature plays its authorization role in almost every document. Proper care should be taken for the verification of the genuineness of the signature in legal documents. Signature verification scheme can be online or offline based on the acquisition type. A novel method for offline signature verification in bank cheques is proposed. It is found out that using fractal dimensions for verification purpose improves the accuracy rate. Also the fundamentals of offline signature verification process are discussed. The proposed system uses connected Components Labeling, Fractal Dimensions and Fuzzy Logic for signature verification. The signature is scanned and preprocessed. Using connected components labeling, the signature is split into regions and each region is labeled uniquely. Feature values for each labeled regions are extracted and normalised. Fractal dimensions of signature images are calculated. Extracted feature values and fractal dimensions are compared with the feature values of the sample signatures for its genuineness. Fuzzy classifies the genuine and forged signatures correctly to its fullest extent. Some signatures may have more noise or it may be complex for the system to identify or classify. Those signatures may need some manual intervention. The proposed verification system shows very good results with good sensitivity and specificity. It has an accuracy of maximum 50%
Non-english and non-latin signature verification systems: A survey
Signatures continue to be an important biometric because they remain widely used as a means of personal verification and therefore an automatic verification system is needed. Manual signature-based authentication of a large number of documents is a difficult and time consuming task. Consequently for many years, in the field of protected communication and financial applications, we have observed an explosive growth in biometric personal authentication systems that are closely connected with measurable unique physical characteristics (e.g. hand geometry, iris scan, finger prints or DNA) or behavioural features. Substantial research has been undertaken in the field of signature verification involving English signatures, but to the best of our knowledge, very few works have considered non-English signatures such as Chinese, Japanese, Arabic etc. In order to convey the state-of-the-art in the field to researchers, in this paper we present a survey of non-English and non-Latin signature verification systems
Introduction to Presentation Attacks in Signature Biometrics and Recent Advances
Applications based on biometric authentication have received a lot of
interest in the last years due to the breathtaking results obtained using
personal traits such as face or fingerprint. However, it is important not to
forget that these biometric systems have to withstand different types of
possible attacks. This chapter carries out an analysis of different
Presentation Attack (PA) scenarios for on-line handwritten signature
verification. The main contributions of this chapter are: i) an updated
overview of representative methods for Presentation Attack Detection (PAD) in
signature biometrics; ii) a description of the different levels of PAs existing
in on-line signature verification regarding the amount of information available
to the impostor, as well as the training, effort, and ability to perform the
forgeries; and iii) an evaluation of the system performance in signature
biometrics under different scenarios considering recent publicly available
signature databases, DeepSignDB and SVC2021_EvalDB. This work is in line with
recent efforts in the Common Criteria standardization community towards
security evaluation of biometric systems.Comment: Chapter of the Handbook of Biometric Anti-Spoofing (Third Edition
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