Sánchez “On-line Handwritten Signature Verification Using Hidden Markov Models (original) (raw)

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.

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

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.

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.

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.

Online Signature Verification Using Vector Quantization and Hidden Markov Model

In this paper an on-line signature verification system, using vector quantization and Hidden Markov Model (VQ-HMM) is presented. After the signature acquisition, a Chebichef filter is used for noise reduction, and size and phase normalization is performed using Fourier transform. Each signature is segmented and mean velocity, acceleration and pressure are used as extracted features. K-means clustering is used for generation a codebook and VQ generates a code word for each signature. These code words are used as observation vectors in training and recognition phase. HMM models are trained using Baum Welch algorithm. In the verification phase, the forward algorithm is used. The Threshold used in the verification phase is a function of the minimum probability in training phase. Equal Error Rate obtained from this system is 14%.

Handwritten Signature Verification Using Complementary Statistical Models

Journal of Computers, 2009

This paper describes a system for performing handwritten signature verification using complementary statistical models. The system analyses both the static features of a signature (e.g., shape, slant, size), and its dynamic features (e.g., velocity, pen-tip pressure, timing) to form a judgment about the signer’s identity. This approach’s novelty lies in combining output from existing Neural Network and Hidden Markov Model based signature verification systems to improve the robustness of any specific approach used alone. The system performs reasonably well and achieves an overall error rate of 2:1% in the best case. The results of several other experiments are also presented including using less reference signatures, allowing multiple signing attempts, zero-effort forgery attempts, providing visual feedback, and signing a password rather than a signature.

Estimating the pen trajectories of static signatures using hidden Markov models

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

Static signatures originate as handwritten images on documents and by definition do not contain any dynamic information. This lack of information makes static signature verification systems significantly less reliable than their dynamic counterparts. This study involves extracting dynamic information from static images, specifically the pen trajectory while the signature was created. We assume that a dynamic version of the static image is available (typically obtained during an earlier registration process). We then derive a hidden Markov model from the static image and match it to the dynamic version of the image. This match results in the estimated pen trajectory of the static image.