Offline Signature Verification: An Approach Based on Score Level Fusion

Prakash, H. N.; Guru, D. S.

doi:10.5120/383-573

Cited by 14 publications

(10 citation statements)

References 15 publications

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“…Using the same signature database, Prakash and Guru extended their work by using k-NN and obtained the FRR of 14.85 % and FAR of 19.82 % [22]. This time, they proposed signature verification based on the score level fusion of distance and orientation features of centroids of the signatures.…”

Section: Resultsmentioning

confidence: 99%

Biometric signature verification system based on freeman chain code and k-nearest neighbor

Azmi

Nasien

Omar

2016

Multimed Tools Appl

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Signature is one of human biometrics that may change due to some factors, for example age, mood and environment, which means two signatures from a person cannot perfectly matching each other. A Signature Verification System (SVS) is a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. Before extracting the features, the raw images will undergo preprocessing stage; binarization, noise removal, cropping and thinning. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated against split image to test the feature efficiency. Finally, verification utilized Euclidean distance to measured and matched in k-Nearest Neighbors. MCYT bimodal database was used in every stage in the system. Based on the experimental results, the lowest error rate for FRR and FAR were 6.67 % and 12.44 % with AER 9.85 % which is better in term of performance compared to other works using that same database.

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Section: Resultsmentioning

confidence: 99%

Biometric signature verification system based on freeman chain code and k-nearest neighbor

Azmi

Nasien

Omar

2016

Multimed Tools Appl

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“…2. In case of grid based approach using SVM as classifier, acceptance rate of genuine signature is better but acceptance rate of forgery signature is approximation of 30 % [10,14]. 3.…”

Section: Motivation and Contribution Of The Proposed Workmentioning

confidence: 99%

“…The Bayes classifier has been used as a matching technique with FRR and FAR of 16.40 % and 14.20 % respectively. Machine learning approach based on score level fusion has been proposed by Prakash et al [14]. They performed their experimental results on GPDS data set and achieved FRR and FAR of 16.40 % and 29.41 % respectively.…”

Section: Review Of Related Workmentioning

confidence: 99%

Verification of static signature pattern based on random subspace, REP tree and bagging

Chandra

Maheshkar

2017

Multimed Tools Appl

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In this paper we propose an effective method for static signature recognition from spontaneous handwritten text images. Our method relies on different aspects of writing: the presence of redundant patterns in the writing and its features. Signatures are analyzed at small fragments in which we seek to extract the patterns that an individual employs frequently as he writes. We exploit different features of writing like orientation, centroid and contour by computing a set of features from writing samples at different levels of observations. Orientation like intersecting point, edge point and gradient change of signature achieve great success in feature description. These features are extracted from the standard signature database and extracted features are trained and tested by machine learning (ML) approach. The machine learning approaches like Bagging, Random subspace (RS) and REP tree are used for classification purpose. Bagging with 10 iterations and base learner achieved efficiency upto 88 %. RS randomly selects features from feature set and creates new feature set. It uses decision tree as base classifier with different tree size. RS achieved efficiency same as Bagging but has more statistical errors. However, in case of REP tree we have achieved efficiency upto 75 %. The experimental results show that the Bagging and RS achieves promising results on publicly available data set.

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“…In [12] authors have found that the contourlet transform could extract better features. Neural Networks based approaches have the advantages of being flexible and adaptive [15]. Neural network based approaches are unified approaches for feature extraction and classification and flexible procedures for finding good, moderately nonlinear solutions.…”

Section: Related Workmentioning

confidence: 99%

“…Another system is based on score level fusion of distance and orientation features of centroids [15]. The proposed method used symbolic representation of offline signatures using bi-interval valued feature vector.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Offline Signature Verification System

Arathi¹,

Govardhan²

2014

IJMLC

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Abstract-Offline signature verification is one of most challenging area of pattern recognition. Many methods have been introduced in literature to find whether a given signature is genuine or forgery. In the proposed work, the signature image is converted into time series data using linear scanning method and then time series shapelets are identified to distinguish genuine signatures from forged ones. The shapelets are time series subsequences which are maximally representative of a class. To compare the time series data, the proposed method uses Mahalanobis distance measure. The experimental results show that the method has great reduction in equal error rate.

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Offline Signature Verification: An Approach Based on Score Level Fusion

Cited by 14 publications

References 15 publications

Biometric signature verification system based on freeman chain code and k-nearest neighbor

Biometric signature verification system based on freeman chain code and k-nearest neighbor

Verification of static signature pattern based on random subspace, REP tree and bagging

An Efficient Offline Signature Verification System

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