Structural description and classification of signature images

Ammar, Maan; Yoshida, Yoshitoku; Fukumura, Teruo

doi:10.1016/0031-3203(90)90093-z

Cited by 42 publications

(30 citation statements)

References 9 publications

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“…[45] presents a signature recognition and verification system based on compression neural networks in combination with positional cuttings of the signature being tested. Other approaches to signature recognition systems, previously outlined in the Introduction Section, are [2], [19], [34] and [39]. As far as we know SVM have not been used for signature recognition, but they have been used in other similar applications like handwritten digit recognition or recognition of some Asian characters.…”

Section: Svm and Mlp For Automatic Off-linementioning

confidence: 99%

“…Other potential signature recognition applications are in law-enforcement applications, where the identification of perpetrators is a fundamental requirement of the solution, and in the analysis of some historical documents [21]. Some previous works in the area of automatic signature recognition are: Ammar et al [2] that uses a hierarchical scheme of signature descriptors to identify a test signature; Han and Sethi [19], which considers a set of geometric and topologic features to map a signature image into two string of finite symbols; Pavlidis et al [34], which proposes the application of active deformable models for approximating the external shape of a signature; and Riba et al [39], that compares different statistical methods, using a feature extraction preprocessing, to carry out the recognition of signatures. From a theoretical point of view, signature recognition and verification are different and independent problems, recognition is a 1:N matching problem while identification is 1:1.…”

Section: Introductionmentioning

confidence: 99%

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Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Frı́as-Martı́nez

Sánchez

Vélez

2006

Engineering Applications of Artificial Intelligence

108

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Abstract. -The problem of automatic signature recognition has received little attention in comparison with the problem of signature verification despite its potential applications for accessing security-sensitive facilities and for processing certain legal and historical documents. This paper presents an efficient off-line human signature recognition system based on Support Vector Machines (SVM) and compares its performance with a traditional classification technique, Multi-Layer Perceptrons (MLP). In both cases we propose two approaches to the problem: (1) construct each feature vector using a set of global geometric and moment-based characteristics from each signature and (2) construct the feature vector using the bitmap of the corresponding signature. We also present a mechanism to capture the intrapersonal variability of each user using just one original signature. Our results empirically show that SVM, which achieves up to 71% correct recognition rate, outperforms MLP.

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Section: Svm and Mlp For Automatic Off-linementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Frı́as-Martı́nez

Sánchez

Vélez

2006

Engineering Applications of Artificial Intelligence

108

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“…Irrespective of the above limitations we can still use signature as our best biometric feature, since the signature is a unique identity of an individual and is being used extensively in practical systems. No two signatures can be identical, unless one of them is a forgery or copy of the other [35]. The signature recognition systems find applications in government, legal and commercial areas.…”

Section: B Signature Recognition Systemmentioning

confidence: 99%

Multimodal Biometric Person Authentication using Speech, Signature and Handwriting Features

Eshwarappa¹,

Latte²

2011

SpecialIssue

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Abstract-The objective of this work is to develop a multimodal biometric system using speech, signature and handwriting information. Unimodal biometric person authentication systems are initially developed for each of these biometric features. Methods are then explored for integrating them to obtain multimodal system. Apart from implementing state-of-the art systems, the major part of the work is on the new explorations at each level with the objective of improving performance and robustness. The latest research indicates multimodal person authentication system is more effective and more challenging. This work demonstrates that the fusion of multiple biometrics helps to minimize the system error rates. As a result, the identification performance is 100% and verification performances, False Acceptance Rate (FAR) is 0%, and False Rejection Rate (FRR) is 0%.

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“…We use the Grid & Texture Information features [25] & successive geometric center with depth 2 (Two Iterations) [3]. Corresponding extracted feature set is shown in Figure 7 & 8.…”

Section: 3grid Texture Features and Successive Geometric Centersmentioning

confidence: 99%

Off-Line Signature Recognition Systems

Kekre¹,

Bharadi²

2010

IJCA

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Handwritten signature is one of the most widely used biometric traits for authentication of person as well as document. In this paper we discuss issues regarding off-line signature recognitions. We review existing techniques, their performance and method for feature extraction. We discuss a system designed using cluster based global features which is a multi algorithmic offline signature recognition system.

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Structural description and classification of signature images

Cited by 42 publications

References 9 publications

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Multimodal Biometric Person Authentication using Speech, Signature and Handwriting Features

Off-Line Signature Recognition Systems

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