Biometric Personal Authentication Based on Handwritten Signals

Lizárraga, Miguel Gustavo; Ling, Lee Luan

doi:10.1007/978-3-540-25948-0_73

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Fang et al [3,4] used stroke positions and positional variations for signature verification with average error rate of 18.1%. Lizarraga et al [5] presented personal authentication method using stroke & contour slopes and achieved error rate of 0.7% and 93.7% correct classification rate.…”

Section: Introductionmentioning

confidence: 98%

A Pattern Matching Classifier for Offline Signature Verification

Bansal

Garg

Gupta

2008

2008 First International Conference on Emerging Trends in Engineering and Technology

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Offline signature verification can be linked as a biometric classifier that categorizes a signature into two classes: genuine and forged. In this paper a contour matching algorithm is proposed that tracks the basic characteristic patterns in a sample signature and verifies it. It capitalizes on the geometrical properties of the signature and takes into account the inevitable intrapersonal variations for the user set A. The system is trained with 8 original signatures and given a test sample; verification is done by a triangle matching algorithm that validates a signature on the basis of the relative position of the critical points (minimum set of points which when provided can retrace any given signature model). Results verify the effectiveness of the proposed mechanism.

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Section: Introductionmentioning

confidence: 98%

A Pattern Matching Classifier for Offline Signature Verification

Bansal

Garg

Gupta

2008

2008 First International Conference on Emerging Trends in Engineering and Technology

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“…Stroke positions and positional variations for signature verification were used by Fang et al [18,19] with an average error rate of 18.1%. Lizarraga et al [20] presented personal authentication method using stroke & contour slopes and achieved an error rate of 0.7% and 93.7% correct classification rate.…”

Section: Current Workmentioning

confidence: 99%

Using content based image retrieval techniques for the indexing and retrieval of Thai handwritten documents

Sangsawad

Fung

2010

2010 the 2nd International Conference on Computer and Automation Engineering (ICCAE)

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Abstract-This paper proposes the use of content base image retrieval (CBIR) techniques for indexing and retrieval of handwritten documents in Thai language. Issues associated with Thai handwritten documents are the lack of spacing between words, multi-level alphabets and different writing styles. This causes low recognition rate based on automated techniques such as Optical Character Recognition (OCR). This paper also examined off-line signature recognition techniques in order to adapt to Thai handwriting system for matching data. The objective of the proposal is to develop a semiautomated method to index and retrieve Thai handwritten documents based on sampled keywords by combining CBIR and signature recognition techniques.

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Classification of handwritten signatures based on name legibility

2007

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Javier Galbally ; Julian Fierrez ; Javier Ortega-Garcia; "Classification of handwritten signatures based on name legibility", Biometric Technology for Human Identification IV, Proc. SPIE 6539 (April 12, 2007); doi:10.1117/12.719236. Copyright 2007 Society of Photo‑Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.Proceedings of the IV Biometric Technology for Human Identification (Orlando, Florida, USA)An automatic classification scheme of on-line handwritten signatures is presented. A Multilayer Perceptron (MLP) with a hidden layer is used as classifier, and two different signature classes are considered, namely: legible and non-legible name. Signatures are represented considering different feature subsets obtained from global information. Mahalanobis distance is used to rank the parameters and feature selection is then applied based on the top ranked features. Experimental results are given on the MCYT signature database comprising 330 signers. It is shown experimentally that automatic on-line signature classification based on the name legibility is feasible.This work was supported by the Spanish Ministry of Science and Technology under project TIC2003-08382-C05-01 and the European Network of Excellence Biosecure (IST-2002-507634)

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Biometric Personal Authentication Based on Handwritten Signals

Cited by 4 publications

References 5 publications

A Pattern Matching Classifier for Offline Signature Verification

A Pattern Matching Classifier for Offline Signature Verification

Using content based image retrieval techniques for the indexing and retrieval of Thai handwritten documents

Classification of handwritten signatures based on name legibility

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