Ergodic HMM-UBM System for On-Line Signature Verification (original) (raw)

HMM-based on-line signature verification: Feature extraction and signature modeling

Pattern Recognition Letters, 2007

A function-based approach to on-line signature verification is presented. The system uses a set of time sequences and Hidden Markov Models (HMMs). Development and evaluation experiments are reported on a subcorpus of the MCYT bimodal biometric database comprising more than 7,000 signatures from 145 subjects. The system is compared to other state-of-the-art systems based on the results of the First International Signature Verification Competition (SVC 2004). A number of practical findings related to feature extraction and modeling are obtained.

Automatic online signature verification using HMMs with user-dependent structure

2007

A novel strategy for Automatic online Signature Verification based on hidden Markov models (HMM) with user-dependent structure is presented in this work. Under this approach, the number of states and Gaussians giving the optimal prediction results are independently selected for each user. With this simple strategy just three genuine signatures could be used for training, with an EER under 2.5% obtained for the basic set of raw signature parameters provided by the acquisition device. This results increment by a factor of six the accuracy obtained with the typical approach in which claim-independent structure is used for the HMMs.

On-line signature verification with two-stage statistical models

Eighth International Conference on Document Analysis and Recognition (ICDAR'05), 2005

Signature verification is a challenging task, because only small set of genuine samples can be acquired and usually no forgeries are available in real application. In this paper, we propose a new two-stage statistical system for automatic on-line signature verification. Our system is composed of a simplified GMM model for global signature features, and a discrete HMM model for local signature features. To be practical, we introduce specific simplification strategies for model building and training. Our system requires only 5 genuine samples for new users and relies on only 3 global parameters for quick and efficient system tuning. The experiment results show that our system works effectively with very small training set of genuine samples only.

A hybrid HMM/ANN based approach for online signature verification

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. Keywords: Hidden Markov Model, Artificial Neural Networks, Online signature verification, Viterbi algorithm

Online Signature Verification Based on Generative Models

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2000

The success of generative models for online signature verification has motivated many research works on this topic. These systems may use hidden Markov models (HMMs) in two different modes: user-specific HMM (US-HMM) and user-adapted universal background models (UBMs) (UA-UBMs). Verification scores can be obtained from likelihood ratios and a distance measure on the Viterbi decoded state sequences. This paper analyzes several factors that can modify the behavior of these systems and which have not been deeply studied yet. First, we study the influence of the feature set choice, paying special attention to the role of dynamic information order, suitability of feature sets on each kind of generative model-based system, and the importance of inclination angles and pressure. Moreover, this analysis is also extended to the influence of the HMM complexity in the performance of the different approaches. For this study, a set of experiments is performed on the publicly available MCYT-100 database using only skilled forgeries. These experiments provide interesting outcomes. First, the Viterbi path evidences a notable stability for most of the feature sets and systems. Second, in the case of US-HMM systems, likelihood evidence obtains better results when lowest order dynamics are included in the feature set, while likelihood ratio obtains better results in UA-UBM systems when lowest dynamics are not included in the feature set. Finally, US-HMM and UA-UBM systems can be used together for improved verification performance by fusing at the score level the Viterbi path information from the US-HMM system and the likelihood ratio evidence from the UA-UBM system. Additional comparisons to other state-of-the-art systems, from the ESRA 2011 signature evaluation contest, are also reported, reinforcing the high performance of the systems and the generality of the experimental results described in this paper.

An off-line signature verification system using hidden Markov model and cross-validation

Proceedings 13th Brazilian Symposium on Computer Graphics and Image Processing (Cat. No.PR00878), 2000

This work has as main objective to present an off-line signature verification system. It is basically divided into three parts. The first one demonstrates a pre-processing process, a segmentation process and a feature extraction process, in which the main aim is to obtain the maximum performance quality of the process of verification of random falsifications, in the false acceptance and false rejection concept. The second presents a learning process based on HMM, where the aim is obtaining the best model. That is, one that is capable of representing each writer's signature, absorbing yet at the same time discriminating, at most the intra-personal variation and the interpersonal variation. A third and last part, presents a signature verification process that uses the models generated by the learning process without using any prior knowledge of test data, in other words, using an automatic derivation process of the decision thresholds.

Application of hidden Markov models for signature verification

Pattern Recognition, 1995

This paper describes a technique for on-line signature verification using Hidden Markov Models (HMMs). Signatures are captured and digitized in real-time using a graphic tablet. For each signature a HMM is constructed using a set of sample signatures described by the normalized directional angle function of the distance along the signature trajectory. The Baum-Welch algorithm is used for both training and classification. Experimental results based on 496 signatures from 31 subjects are presented which show that HMM technique is very potential for signature verification. HMMs Baum-Welch algorithm Signature verification Forward probability Backward probability

Online Handwritten Signature Verification Using Hidden Markov Models

Lecture Notes in Computer Science, 2003

Most people are used to signing documents and because of this, it is a trusted and natural method for user identity verification, reducing the cost of password maintenance and decreasing the risk of eBusiness fraud. In the proposed system, identity is securely verified and an authentic electronic signature is created using biometric dynamic signature verification. Shape, speed, stroke order, off-tablet motion, pen pressure and timing information are captured and analyzed during the real-time act of signing the handwritten signature. The captured values are unique to an individual and virtually impossible to duplicate. This paper presents a research of various HMM based techniques for signature verification. Different topologies are compared in order to obtain an optimized high performance signature verification system and signal normalization preprocessing makes the system robust with respect to writer variability.

Sánchez “On-line Handwritten Signature Verification Using Hidden Markov Models

2003

Abstract. Most people are used to signing documents and because of this, it is a trusted and natural method for user identity verification, reducing the cost of password maintenance and decreasing the risk of eBusiness fraud. In the proposed system, identity is securely verified and an authentic electronic signature is created using biometric dynamic signature verification. Shape, speed, stroke order, off-tablet motion, pen pressure and timing information are captured and analyzed during the real-time act of signing the handwritten signature. The captured values are unique to an individual and virtually impossible to duplicate. This paper presents a research of various HMM based techniques for signature verification. Different topologies are compared in order to obtain an optimized high performance signature verification system and signal normalization preprocessing makes the system robust with respect to writer variability. 1

Markov Model-Based Handwritten Signature Verification

IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, 2008

Biometric security devices are now permeating all facets of modern society. All manner of items including passports, driver’s licences and laptops now incorporate some form of biometric data and/or authentication device. As handwritten signatures have long been considered the most natural method of verifying one’s identity, it makes sense that pervasive computing environments try to capitalise on the use of automated Handwritten Signature Verification systems (HSV). This paper presents a HSV system that is based on a Hidden Markov Model (HMM) approach to representing and verifying the hand signature data. HMMs are naturally suited to modelling flowing entities such as signatures and speech. The resulting HSV system performs reasonably well with an overall error rate of 3.5% being reported in the best case experimental analysis.