Writer-independent off-line signature verification using surroundedness feature

Kumar, Rajesh; Sharma, J. D.; Chanda, Bhabatosh

doi:10.1016/j.patrec.2011.10.009

Cited by 185 publications

(59 citation statements)

References 28 publications

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“…The evaluation was carried out on all the 55 sets. Table 1 compares the error rates and the accuracy against the existing methods, of which [8] and [9] are person independent. FRR keeps decreasing from FRBS contrast intensification to FRFS in the proposed methods implying that forgeries are being identified more accurately by contrast enhancement and feature selection.…”

Section: Resultsmentioning

confidence: 99%

“…Kumar et al [8] proposed a set of morphological features for extraction and multi-layer perceptron (MLP) and support vector machine (SVM) for classification and obtained an accuracy of 88.4%. Kumar et al [9] proposed a set of features based on Surroundedness property of a signature image and based on MLP and SVM obtained an accuracy of 91.67%.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Fuzzy Hybrid Framework for Offline Signature Verification

Ganapathi¹,

Nadarajan²

2013

Lecture Notes in Computer Science

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Abstract. Signatures are widely used means of personal verification. This paper presents a fuzzy hybrid framework based person-dependent off-line signature verification using fuzzy inference rules in image contrast enhancement, fuzzy rough reduction for feature selection and Simplified fuzzy ARTMAP for verification. Three sets of experimental studies are conducted on CEDAR benchmark dataset and the results reported are comparable to other techniques in terms of classification accuracy and time.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Fuzzy Hybrid Framework for Offline Signature Verification

Ganapathi¹,

Nadarajan²

2013

Lecture Notes in Computer Science

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“…Some of them have employed two or three expert systems that evaluate the signature in two/three different ways and verify whether it is genuine or forge. The Writer-independent (Kumar et al, 2012) approach is based on surroundedness property of a signature. The proposed method mainly concentrates on shape of a signature.…”

Section: Literature Reviewmentioning

confidence: 99%

Blockwise Binary Pattern: A Robust and an Efficient Approach For Offline Signature Verification

Shekar

Pilar

Sunil

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents a variant of local binary pattern called Blockwise Binary Pattern (BBP) for the offline signature verification. The proposed approach has three major phases : Preprocessing, Feature extraction and Classification. In the feature extraction phase, the signature is divided into 3 x 3 neighborhood blocks. A BBP value for central pixel of each block is computed by considering its 8 neighboring pixels and the 3 x 3 block is replaced by this central pixel. To compute BBP value for each block, a binary sequence is formed by considering 8 neighbors of the central pixel, by following the pixels in a anti-clockwise direction. Then the minimum decimal equivalent of this binary sequence is computed and this value is assigned to the central pixel. The central pixel is merged with the neighboring 8 pixels representing the 3 X 3 neighborhood block. This method is found to be invariant to rotation, scaling and shift of the signature. The features are stored in the form of normalized histogram. The SVM classifier is used for the signature verification. Experiments have been performed on standard signature datasets namely CEDAR and GPDS which are publicly available English signature datasets and on MUKOS, a regional language (Kannada) dataset and compared with the well-known approaches to exhibit the performance of the proposed approach.

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“…In [4], Rajesh kumar etal proposed a signature verification system using the surroundedness feature and CEDAR database and achieved 91.67 % of accuracy. Hai-Lin and Hai-zhou [5] proposed a verification method using Zernike moments since it is rotation and scale invariant.…”

Section: Literature Reviewmentioning

confidence: 99%