A writer-independent off-line signature verification system based on signature morphology

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

doi:10.1145/1963564.1963610

Cited by 55 publications

(17 citation statements)

References 14 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%

“…Larkins et al [10] introduced adaptive feature thresholding (AFT) combined with spatial pyramids [11] and equimass sampling grids [3] for feature extraction using gradient direction and achieved 90% accuracy. 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%

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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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“…Several applications have been developed based on one of the accurate classifier called Support Vector Machine(SVM). The goal of SVM is to produce a model (based on the training data) which predicts the target values of the test data given only the test data attributes [ (Hsu et al, 2003), (Kumar et al, 2010a)] . Any classification task usually involves separating data into training and testing sets.…”

Section: Classification Based On Svmmentioning

confidence: 99%

“…From the literature we observed that, Kalera et al, (Kalera et al, 2004), Chen and Shrihari (Chen and Srihari, 2005) and Kumar et al, (Kumar et al, 2010b) have experimented on CEDAR dataset and hence a comparative analysis is given in Table 3.…”

Section: Experimentation On Cedar Datasetmentioning

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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“…Solar et al [11] concentrated on local interest points and descriptors for off-line signature verification. Kumar et al [7] presents a novel set of features based on surroundedness property of a signature image to provide a measure of texture through the correlation among signature pixels. To examine the efficacy they used two popular classifiers, SVM and MLP on CEDAR and GPDS dataset.…”

Section: Introductionmentioning

confidence: 99%

Off-Line Signature Verification Based on Principal Component Analysis and Multi-Layer Perceptrons

Shekar

Bharathi²

2014

Advances in Intelligent Systems and Computing

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Abstract. The off-line signature verification approach based on Principal Component Analysis (PCA) and Muti-Layer Perceptrons (MLP) is proposed in this work. The proposed approach involves three major phases. In the first phase, the signature image is subjected to preprocessing, such as binarization, noise elimination, skew correction followed by normalising the image by a series of thinning and dilating with a structuring element of size 3X3. The principal component analysis is employed on the preprocessed signature samples and the features extracted are termed as eigen-sign feature vectors, in second phase. The multilayer perceptrons, a neural network based approach is trained with eigen-sign feature vector and subsequently used for verification of signature samples. Extensive experimentation has been conducted on the publicly available signature datasets namely, CEDAR, GPDS-100 and MUKOS, a regional language dataset. The state-of-art off-line signature verification methods are considered for comparative study and objective analysis through experimental results is provided to justify the accuracy of the proposed approach.

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A writer-independent off-line signature verification system based on signature morphology

Cited by 55 publications

References 14 publications

A Fuzzy Hybrid Framework for Offline Signature Verification

A Fuzzy Hybrid Framework for Offline Signature Verification

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

Off-Line Signature Verification Based on Principal Component Analysis and Multi-Layer Perceptrons

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