scispace - formally typeset
Search or ask a question
Posted Content

Offline Signature Identification by Fusion of Multiple Classifiers using Statistical Learning Theory

TL;DR: This paper uses Support Vector Machines (SVM) to fuse multiple classifiers for an offline signature system and the results are found to be promising.
Abstract: This paper uses Support Vector Machines (SVM) to fuse multiple classifiers for an offline signature system. From the signature images, global and local features are extracted and the signatures are verified with the help of Gaussian empirical rule, Euclidean and Mahalanobis distance based classifiers. SVM is used to fuse matching scores of these matchers. Finally, recognition of query signatures is done by comparing it with all signatures of the database. The proposed system is tested on a signature database contains 5400 offline signatures of 600 individuals and the results are found to be promising.
Citations
More filters
Journal Article
TL;DR: Previous work in the field of signature and writer identification is presented to show the historical development of the idea and a new promising approach in handwritten signature identification based on some basic concepts of graph theory is defined.
Abstract: Handwritten signature is being used in various applications on daily basis. The problem arises when someone decides to imitate our signature and steal our identity. Therefore, there is a need for adequate protection of signatures and a need for systems that can, with a great degree of certainty, identify who is the signatory. This paper presents previous work in the field of signature and writer identification to show the historical development of the idea and defines a new promising approach in handwritten signature identification based on some basic concepts of graph theory. This principle can be implemented on both on-line handwritten signature recognition systems and off-line handwritten signature recognition systems. Using graph norm for fast classification (filtration of potential users), followed by comparison of each signature graph concepts value against values stored in database, the system reports 94.25% identification accuracy.

33 citations

Book ChapterDOI
01 Jan 2018
TL;DR: This chapter introduces an offline signature recognition technique using rough neural network and rough set, a new hybrid technique that achieves good results, since the short rough Neural Network algorithm is neglected by the grid features technique, and then the advantages of both techniques are integrated.
Abstract: This chapter introduces an offline signature recognition technique using rough neural network and rough set. Rough neural network tries to find better recognition performance to classify the input offline signature images. Rough sets have provided an array of tools which turned out to be especially adequate for conceptualization, organization, classification, and analysis of various types of data, when dealing with inexact, uncertain, or vague knowledge. Also, rough sets discover hidden pattern and regularities in application. This new hybrid technique achieves good results, since the short rough neural network algorithm is neglected by the grid features technique, and then the advantages of both techniques are integrated.

25 citations

Journal ArticleDOI
TL;DR: Adaptive Window Positioning technique which focuses on not just the meaning of the handwritten signature but also on the individuality of the writer is proposed which can be used to detect signatures signed under emotional duress.
Abstract: The paper presents to address this challenge, we have proposed the use of Adaptive Window Positioning technique which focuses on not just the meaning of the handwritten signature but also on the individuality of the writer. This innovative technique divides the handwritten signature into 13 small windows of size nxn(13x13).This size should be large enough to contain ample information about the style of the author and small enough to ensure a good identification performance.The process was tested with a GPDS data set containing 4870 signature samples from 90 different writers by comparing the robust features of the test signature with that of the user signature using an appropriate classifier. Experimental results reveal that adaptive window positioning technique proved to be the efficient and reliable method for accurate signature feature extraction for the identification of offline handwritten signatures.The contribution of this technique can be used to detect signatures signed under emotional duress.

14 citations

Proceedings ArticleDOI
10 Nov 2011
TL;DR: The proposed technique is based on the grid features extraction and deals with skilled forgeries and has been tested on two databases: Database A and a standard Database B (Set 1 and Set 2).
Abstract: Signature verification is one of the most widely used biometrics for authentication. This paper presents a novel approach for offline signature verification. The proposed technique is based on the grid features extraction. For verification, the extracted features of test signature are compared with the already trained features of the reference signature. This technique is suitable for various applications such as bank transactions, passports etc. The threshold used in the proposed technique can be dynamically changed according to the target application. Basically, the threshold here is the security level which the user can input as per his requirement. The proposed technique deals with skilled forgeries and has been tested on two databases: Database A and a standard Database B (Set 1 and Set 2). The proposed technique gives FAR of 9.7% and FRR of 17.9% for Database A, FAR of 12.6% and FRR of 10.2% for Database B (Set 1) and FAR of 13.5% and FRR of 10.8% for Database B (Set 2) which is better than many existing verification techniques.

14 citations

Proceedings ArticleDOI
08 Jan 2014
TL;DR: A novel approach for off-line signature recognition system is presented in this work, which is based on local radon features, where totally 16 radon transform based projection features are extracted which are used to distinguish the different signatures.
Abstract: A novel approach for off-line signature recognition system is presented in this work, which is based on local radon features. The proposed system functions in three stages. Pre-processing stage, which consists of three steps: gray scale conversion, binarisation and fitting boundary box in order to make signatures ready for feature extraction, Feature extraction stage, where totally 16 radon transform based projection features are extracted which are used to distinguish the different signatures. Finally in Neural Network stage, an efficient Back Propagation Neural Network (BPNN) is designed and trained with 16 extracted features. The trained Neural Network is further used for signature recognition after the process of feature extraction. The average recognition accuracy obtained using this model ranges from 97%-87% with the training set of 10-40 persons.

12 citations

References
More filters
Book
Vladimir Vapnik1
01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

40,147 citations

Journal ArticleDOI
TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
Abstract: A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.

4,678 citations


"Offline Signature Identification by..." refers background in this paper

  • ...Introduction The use of biometric technologies [1] for human identity verification is growing rapidly in the civilized society and showing its advancement towards usability of biometric security artifacts....

    [...]

Book
10 Mar 2005
TL;DR: This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.
Abstract: A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

3,821 citations


"Offline Signature Identification by..." refers background in this paper

  • ...Offline signature verification [2], [3] in comparison with other biometric traits such as fingerprint [4], face [7], palmprint [6], iris [5], etc has the advantage of wide acceptance....

    [...]

Journal ArticleDOI
TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.
Abstract: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. The paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea.

2,829 citations

Proceedings ArticleDOI
10 Dec 2002
TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.
Abstract: The principle that underlies the recognition of persons by their iris patterns is the failure of a test of statistical independence on texture phase structure as encoded by multiscale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. Algorithms first described by the author in 1993 have now been tested in several independent field trials and are becoming widely licensed. This presentation reviews how the algorithms work and presents the results of 9.1 million comparisons among different eye images acquired in trials in Britain, the USA, Korea, and Japan.

2,437 citations


"Offline Signature Identification by..." refers background in this paper

  • ...Offline signature verification [2], [3] in comparison with other biometric traits such as fingerprint [4], face [7], palmprint [6], iris [5], etc has the advantage of wide acceptance....

    [...]