A fundamental problem in the field of off-line signature verification is the lack of a signature representation based on shape descriptors and pertinent features. The main difficulty lies in the local variability of the writing trace of the signature which is closely related to the identity of human beings. In this paper, we propose a new formalism for signature representation based on visual perception. A signature image consists of 512 128 pixels and is centered on a grid of rectangular retinas which are excited by local portions of the signature. Granulometric size distributions are used for the definition of local shape descriptors in an attempt to characterize the amount of signal activity exciting each retina on the focus of the attention grid. Experimental evaluation of this scheme is made using a signature database of 800 genuine signatures from 20 individuals. Two types of classifiers, a Nearest Neighbor and a threshold classifier, show a total error rate below 0.02 percent and 1.0 percent, respectively, in the context of random forgeries. Index Terms---Off-line signature verification, feature extraction, shape analysis, mathematical morphology. ------------------------------ F ------------------------------ 1I NTRODUCTION N the field of pattern recognition, off-line signature verification is still an open problem [15], [24], [28]. Moreover, the complete elimination of random forgeries, defined as genuine signatures of other writers enrolled in the verification system, is a prerequisite for real applications. A brief analysis reveals that this is a very easy task for human beings, especially because no attempt to imitate the target signature is involved for this class of forgeries [25]. We would like to know why this pattern recognition task is so difficul...

There is considerable interest in authentication based on handwritten signature verification (HSV) because HSV is superior to many other biometric authentication techniques e.g. finger prints or retinal patterns, which are reliable but much more intrusive and expensive. This paper presents a review of dynamic HSV techniques that have been reported in the literature. The paper also discusses possible applications of HSV, lists some commercial products that are available and suggests some areas for future research. 1 1 Introduction Our society is increasingly dependent on electronic storage and transmission of information and this has created a need for electronically verifying a person 's identity. Handwritten signatures have been the normal and customary way for identity verification. Although there have been occasional disputes about the authorship of handwritten signatures (Osborn, 1929; Harrison, 1958; Hilton, 1956), verification of handwritten signatures has not been a major p...

This paper describes a novel approach for signature verification and identification in an offline environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction. These features when used at the word level, instead of the character level, yield promising results with accuracies as high as 78 % and 93 % for verification and identification, respectively. This method was successfully employed in our previous theory of individuality of handwriting developed at CEDAR — based on obtaining within and between writer statistical distance distributions. In this paper, exploring signature verification and identification as offline handwriting verification and identification tasks respectively, we depict a mapping from the handwriting domain to the signature domain.

viii 1 Introduction 1 1.1 Handwritten Character Recognition : : : : : : : : : : : : : : : : : 1 1.2 Related Work : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 1.2.1 Feature Extraction : : : : : : : : : : : : : : : : : : : : : : 4 1.2.2 Handwriting Recognition : : : : : : : : : : : : : : : : : : : 6 1.3 Proposed Approach : : : : : : : : : : : : : : : : : : : : : : : : : : 9 1.4 Thesis Organization : : : : : : : : : : : : : : : : : : : : : : : : : : 12 2 Recognition and Representation of Numeral Patterns 13 2.1 Recognition Based on Human Logical Understanding : : : : : : : 13 2.1.1 Local Geometric Shape Features : : : : : : : : : : : : : : : 14 2.1.2 Learning of Different Contributions Among Local Shape Features : : : : : : : : : : : : : : : : : : : : : : : : : : : : 17 2.1.3 Learning of New Variants by Feature Generation : : : : : 17 2.2 Invariance Based on Biological Visual System : : : : : : : : : : : 18 2.2.1 Log-Polar Transformation : : : : : : : : : : : : : : : : :...

Signatures may be stylish or unconventional and have many personal characteristics that are challenging to reproduce by anyone other then the original author. For this reason, signatures are used and accepted as proof of authorship or consent on personal checks, credit purchases and legal documents. Currently signatures are verified only informally inmanyenvironments, but the rapid development of computer technology has stimulated great interest in research on automated signature veri cation and forgery detection. In this thesis, we focus on forgery detection of off-line signatures. Although a great deal of work has been done on off-line signature verification over the past two decades, the field is not as mature as on-line verification. Temporal information used in on-line verification is not available off-line and the subtle details necessary for off-line verification are embedded at the stroke level and are hard to recover robustly. We approach the off-line problem by establishing a local correspondence between a model and a questioned signature. The questioned signature is segmented into consecutive stroke segments that are matched to the stroke segments of the model. The cost of the match is determined by comparing a set of geometric properties of the corresponding sub-strokes and computing a weighted sum of the property value differences. The least invariant features of the least invariant sub-strokes are given the biggest

Abstract. In this paper, a problem of personal authentication through the use of signature recognition is described. The methods of verification include both online (or dynamic) and off-line (static) signature verification algorithms. The dynamic methods covered, are based on the analysis of the shape, speed, stroke, pen pressure and timing information. While the static methods involve general shape recognition techniques. The paper gives a brief historical overview of the existing methods and presents some of the recent research in the field.

Biometrics can be classified into two broad categories— behavioral (signature verification, keystroke dynamics, etc.) and physiological (iris characteristics, fingerprint, etc.). Handwritten signature is amongst the first few biometrics to be used even before the advent of computers. Signature verification is widely studied and discussed using two approaches [5]. On-line approach uses an electronic tablet and a stylus connected to a computer to extract information about a signature and takes dynamic information like; pressure, velocity, etc whereas in offline approach stable dynamic variations are not used for verification purpose. Offline systems are more applicable and easy to use in comparison with on-line systems in many parts of the world however it is considered more difficult than on-line verification due to the lack of dynamic information. The paper presents a survey of off-line signature verification approaches being followed in different areas. This being a nascent area under research, the survey covers some of the examples of the ways