Application of HMM to Online Signature Verification Based on Segment Differences

Zou, Jie; Wang, Zengfu

doi:10.1007/978-3-319-02961-0_53

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…A function based approach generally takes a longer matching time compared to a parametric based approach yet resulted in lower error rate. In literature we can see the application of various classifiers for online signature such as SVM [15,32] , neural networks [1,5] , HMM [2,3,12] , Parzen window [29,30,43] , distance based [4,35] , random forest [16] and symbolic classifier [17,32] . Further, fusion based approaches are also proposed.…”

Section: Introductionmentioning

confidence: 99%

Online signature verification based on writer dependent features and classifiers

Manjunatha

Manjunath

Guru

et al. 2016

Pattern Recognition Letters

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a b s t r a c tIn this work, an approach for online signature verification based on writer specific features and classifier is investigated. Existing models for online signatures are generally writer independent, as a common classifier or fusion of classifier is used on a common set of features for all writers during verification. In contrast, our approach is based on the usage writer dependent features as well as writer dependent classifier. The two decisions namely optimal features suitable for a writer and a classifier to be used for authenticating the writer are taken based on the error rate achieved with the training samples. The performance of our model is tested on both MCYT-100 (DB1), a sub corpus of MCYT data set, consisting of signatures of 100 writers, MCYT-330 (DB2) consisting of signatures of all 330 writers and visual subcorpus of SUSIG dataset. Experimental results confirm the effectiveness of writer dependent characteristics for online signature verification. The error rate that we achieved is lower when compared to many existing contemporary works on online signature verification especially when the number of training samples available for each writer is sufficient enough.

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Section: Introductionmentioning

confidence: 99%

Online signature verification based on writer dependent features and classifiers

Manjunatha

Manjunath

Guru

et al. 2016

Pattern Recognition Letters

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“…For online signature verification, many classifiers have been attempted by different researchers such as distance based classifier [5], HMM [6,7], SVM [8], PNN [9], Bayesian [10], Symbolic classifier [11], Random Forest [8]. The performance of a verification system is measured in terms of two error rates namely false acceptance rate (FAR) and false rejection rate (FAR).…”

Section: Introductionmentioning

confidence: 99%

Cluster Dependent Classifiers for Online Signature Verification

Manjunath

Manjunatha

Guru

et al. 2015

Mining Intelligence and Knowledge Exploration

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Abstract. In this paper, the applicability of notion of cluster dependent classifier for online signature verification is investigated. For every writer, by the use of a number of training samples, a representative is selected based on minimum average distance criteria (centroid) across all the samples of that writer. Later k-means clustering algorithm is employed to cluster the writers based on the chosen representatives. To select a suitable classifier for a writer, the equal error rate (EER) is estimated using each of the classifier for every writer in a cluster. The classifier which gives the lowest EER for a writer is selected to be the suitable classifier for that writer. Once the classifier for each writer in a cluster is decided, the classifier which has been selected for a maximum number of writers in that cluster is decided to be the classifier for all writers of that cluster. During verification, the authenticity of the query signature is decided using the same classifier which has been selected for the cluster to which the claimed writer belongs. In comparison with the existing works on online signature verification, which use a common classifier for all writers during verification, our work is based on the usage of a classifier which is cluster dependent. On the other hand our intuition is to recommend to use a same classifier for all and only those writers who have some common characteristics and to use different classifiers for writers of different characteristics. To demonstrate the efficacy of our model, extensive experiments are carried out on the MCYT online signature dataset (DB1) consisting signatures of 100 individuals. The outcome of the experiments being indicative of increased performance with the adaption of cluster dependent classifier seems to open up a new avenue for further investigation on a reasonably large dataset.

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A Dynamic Time Warping and Deep Neural Network Ensemble for Online Signature Verification

Gwetu

2021

Machine Learning for Networking

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Application of HMM to Online Signature Verification Based on Segment Differences

Cited by 7 publications

References 12 publications

Online signature verification based on writer dependent features and classifiers

Online signature verification based on writer dependent features and classifiers

Cluster Dependent Classifiers for Online Signature Verification

A Dynamic Time Warping and Deep Neural Network Ensemble for Online Signature Verification

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