A Hidden Markov Model approach to online handwritten signature verification

Kashi, Ramanujan S.; Hu, Jianying; Nelson, W. L.; Turin, William

doi:10.1007/s100320050010

Cited by 64 publications

(36 citation statements)

References 6 publications

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“…Besides, to get more discriminative, two dynamic features, trajectory tangent angle ߠ ௧ and instantaneous velocitiyߥ ௧ , which two are difficult to reproduce based only on visual inspection [9], are computed as follows:…”

Section: Feature Select and Data Processingmentioning

confidence: 99%

The Application Of Bayes Ying-Yang Harmony Based Gmms In On-Line Signature Verification

Zhao¹,

Liu²

2014

IJAIA

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In this contribution, a Bayes Ying-Yang(BYY) harmony based approach for on-line signature verification is presented. In the proposed method, a simple but effective Gaussian Mixture Models(GMMs) is used to represent for each user's signature model based on the prior information collected. Different from the early works, in this paper, we use the Bayes Ying Yang machine combined with the harmony function to achieve Automatic Model Selection(AMS) during the parameter learning for the GMMs, so that a better approximation of the user model is assured. Experiments on a database from the First InternationalSignature Verification Competition (SVC 2004) confirm that this combined algorithm yields quite a satisfactory result.

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Section: Feature Select and Data Processingmentioning

confidence: 99%

The Application Of Bayes Ying-Yang Harmony Based Gmms In On-Line Signature Verification

Zhao¹,

Liu²

2014

IJAIA

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“…The other factor of (5) is immediately related to the local probability, which can be factorized into: P(q1L ,q| X) p(q X)p(ql ql, X)L p(qT q>L ,qf q'TX (7) Now each factor of (7) can be simplified by relaxing the conditional constraint; especially, in the following the factors of (7) are assumed to only depend on the previous state and on a signal window with width 2p+l. In fact, the local probability is simplified as p(ql q,L , q-l, x) = p(qlI q -,X+P ) (8) The following dynamic programming recurrence holds: P(qjX1 ) = max[P(qk X1 )p(q, Xn qk)] (9) Where k runs over all possible states before states ql, and P(q, X7n) denotes the cumulated best path probability of reaching state ql with emitting the partial sequence xl .…”

Section: Feature Extractionmentioning

confidence: 99%

“…For online signature verification, so far there have been many widely employed methods, for example, Neural Network [2,3], the Euclidean Distance Classifiers, dynamic time warping (DTW) [4,5], the hidden Markov models (HMM) [6,7], etc. Generally, the DTW is regarded as a popular method, but it usually suffers from the following two drawbacks: i) Heavy computational load and ii) Warping forgeries [8].…”

mentioning

confidence: 99%

A Hybrid HMM/ANN Based Approach for Online Signature Verification

Quan

Huang

Liu

et al. 2007

2007 International Joint Conference on Neural Networks

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This paper presents a new approach based on HMM/ANN hybrid for online signature verification. A group of ANNs are used as local probability estimators for an HMM. The Viterbi algorithm is employed to work out the global posterior probability of a model. The proposed HMM/ANN hybrid has a strong discriminant ability, i.e, from a local sense, the ANN can be regarded as an efficient classifier, and from a global sense, the posterior probability is consistent with that of a Bayes classifier. Finally, the experimental results show that this approach is promising and competing.

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“…Signature verification is an active research field with application like validation of checks and other financial documents. Due to practical significance of signature verification a lot of research has been carried out and many techniques like dynamic time warping [4], Baysian classifiers [5], Neural networks [6], support vector machine [7], Hidden Markov Model [8] have been already recommended [9] investigated spatial properties of handwritten images through matrix analysis. For details of progress in online signature verification, the readers are referred to a review paper [10].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Offline Handwritten Signatures

Prakash¹,

Guru²

2010

IJCA

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Similarity retrieval of images is an important task in database applications. In such applications, effective organization and retrieval of images can be achieved through indexing. In this paper, the problem of quick retrieval of offline signatures in the context of database of signature images is addressed. The proposed methodology retrieves signatures in the database of signature images for a given query signature according to the decreasing order of their spatial similarity with the query. Similarity computed is based on orientations of corresponding edges drawn in between geometric centers (centroids) of the signature image. We retrieve the best hypotheses in a simple yet efficient way to speed up the subsequent robust recognition stage. The runtime of the signature recognition process is reduced, because the scanning of the entire database for a given query is narrowed down to comparing the query with a few top retrieved hypotheses. The experimentation conducted on a large MCYT_signature database [1] has shown promising results. The results demonstrate the efficacy of the proposed methodology.

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A Hidden Markov Model approach to online handwritten signature verification

Cited by 64 publications

References 6 publications

The Application Of Bayes Ying-Yang Harmony Based Gmms In On-Line Signature Verification

The Application Of Bayes Ying-Yang Harmony Based Gmms In On-Line Signature Verification

A Hybrid HMM/ANN Based Approach for Online Signature Verification

Retrieval of Offline Handwritten Signatures

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