A Markov Chain Monte Carlo Algorithm for Bayesian Dynamic Signature Verification

Muramatsu, Daigo; Kondo, Mitsuru; Sasaki, Masahiro; Tachibana, Satoshi; Matsumoto, Takashi

doi:10.1109/tifs.2005.863507

Cited by 44 publications

(8 citation statements)

References 28 publications

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“…Biometric information has been widely investigated in traditional security systems such as identity verification based on iris [13], fingerprint [14], face [15], handwriting [16]. Unlike traditional biometric cryptosystems in generic networks, the blood circulation system in a human body forms a unique secure communication path specifically available for WBAN [11].…”

Section: Hmm Based Authentication and Selective Encryption Appromentioning

confidence: 99%

An Integrated Biometric-Based Security Framework Using Wavelet-Domain HMM in Wireless Body Area Networks (WBAN)

Wang

Fang

Xing

et al. 2011

2011 IEEE International Conference on Communications (ICC)

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Abstract-In this paper, an integrated biometric-based security framework is proposed for wireless body area networks, which takes advantage of biometric features shared by body sensors deployed at different positions of a person's body. The data communications among these sensors are secured via the proposed authentication and selective encryption schemes that require low computational power and less resources (e.g., battery and bandwidth). Specifically, a wavelet-domain Hidden Markov Model (HMM) classification method is utilized for accurate authentication based on the non-Gaussian statistics of ECG (electrocardiogram) signals. In addition, the biometric information such as ECG signal is used as the biometric key for the encryption in the framework. Our experimental results demonstrate that the proposed approach can achieve accurate authentication performance without extra requirements of key distribution and strict time synchronization.

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Section: Hmm Based Authentication and Selective Encryption Appromentioning

confidence: 99%

An Integrated Biometric-Based Security Framework Using Wavelet-Domain HMM in Wireless Body Area Networks (WBAN)

Wang

Fang

Xing

et al. 2011

2011 IEEE International Conference on Communications (ICC)

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“…Mitsuru Kondo et al [60] proposed a DSV approach by performing nonlinear separation using Bayesian Markov Chain Monte Carlo (MCMC). Daigo Muramatsu et al [46] used a similar approach to that proposed in [60] except that they used four distance measure. The fourth distance measure is the data length difference.…”

Section: Hidden Markov Model (Hmm)mentioning

confidence: 99%

Online Signature Verification: State of the art

El-Henawy¹,

Rashad²,

Nomir³

et al. 2005

IJCT

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ABSTRACTonline or Dynamic signature verification (DSV) is one of the most acceptable, intuitive, fast and cost effective tool for user authentication. DSV uses some dynamics like speed, pressure, directions, stroke length and pen-ups/pen-downs to verify the signer's identity. The state of the art in DSV is presented in this paper. several approaches for DSV are compared and the most influential techniques in this field are highlighted. We concentrate on the relationship between the verification approach used (the nature of the classifier) and the type of features that are used to represent the signature. Literature ReviewThe signing process can be described at two levels: at the high level, the signing method is recovered from long-term memory; parameters are then specified such as size, shape, timing; finally, at a peripheral level, commands are generated for the biophysical muscles. So, the signing process is believed to be a reflex action (ballistic action), rather than a deliberate action. Ballistic handwriting is characterized by a spurt of activity, without positional feedback, whereas deliberate handwriting is characterized by a conscious attempt to produce a visual pattern with the aid of positional feedback [25]. The problem arises here is that many signers can produce their signatures both ballistically and deliberately based on the nature and importance of the task. The process of Signature verification is a two-class problem where the input signature is classified as genuine or forged (i.e. belonging to an impostor). The simplest approach to compare two signatures is to compute correlations between the test signature values and the reference signature values. Such point-topoint comparison usually does not work well since there are a large number of variations between the two signatures and the correlation can be affected by the translation, rotation or scaling of the signature . The decision is made by comparing the similarity score between the input and the enrolled signatures with a predetermined threshold. A tablet device is used to capture dynamic information of a signature. Such dynamic information is a set of sequences of sampled points over time. Each sequence depicts an action performed during the signing process. Signatures from the same person may have different trajectory lengths due to local stretching, compression, omission or additional parts, and hence they are represented by feature vectors of differing lengths. Therefore, straight forward methods, such as the Euclidian distance or autocorrelation, are not very useful in calculation of the dissimilarity value between two signatures [6]. Such point-to-point comparison usually does not work because some portions of any two genuine signatures of the same person may vary significantly. To overcome the problem, non-linearly approaches such as Dynamic Time Warping (DTW) algorithm and Hidden Markov Models (HMM) are commonly used in aligning two signatures. Plamondon and Lorette [141] categorized the various signature verification methods int...

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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 Markov Chain Monte Carlo Algorithm for Bayesian Dynamic Signature Verification

Cited by 44 publications

References 28 publications

An Integrated Biometric-Based Security Framework Using Wavelet-Domain HMM in Wireless Body Area Networks (WBAN)

An Integrated Biometric-Based Security Framework Using Wavelet-Domain HMM in Wireless Body Area Networks (WBAN)

Online Signature Verification: State of the art

Cluster Dependent Classifiers for Online Signature Verification

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