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Signature verification using kolmogorov-smirnov statistic
Abstract
Automatic signature verification of scanned documents are presented here. The strat-egy used for verification is applicable in scenarios where there are multiple knowns(genuine signature samples) from a writer. First the learning process invovles learning the variation and similarities from the known genuine samples from the given writer and then classifica-tion problem answers the question whether or not a given questioned sample belongs to the ensemble of known samples or not. The learning strategy discussed, compares pairs of sig-nature samples from amongst the knwon samples, to obtain a distribution in distance space, that represents the distribution of the variation amongst samples, for that particular writer. The corresponding classification method involves comparing the questioned sample, with all the available knowns, to obtain another distribution in distance space. The classifica-tion task is now to compare the two distributions to obtain a probability of similarity of the two distributions, that represents, the probability of the questioned sample belonging to the ensemble of the knowns. The above strategies are applied to the problem of signature verification and performance results are presented.
... In order to evaluate the level of degradation of traffic signs, we have taken into account the color distributions of the pixels. We adopted a statistical approach for the analysis, based on the Kullback-Leibler [15] divergence and Kolmogorov-Smirnov [16] test. A comparison of the two methods has been performed in order to select the most appropriate one as a good solution for automatic recognition of road signs degradation. ...
... accuracy = tp + tn tp + tn + f p + f n (16) where tp are the true positive, tn are the true negative, f p are the false positive and f n are the false negative values. ...
This paper describes a software application based on statistical methods for the automatic recognition of traffic sign deterioration. The evaluation of traffic sign degradation is usually performed by devices applied on top of the road sign surface, measuring color parameters such as chromatic coordinates and the luminance factor. Moreover, the devices can only check a small fraction of the traffic sign surface at a time, requiring several acquisitions on the same traffic sign. In order to reduce the costs related to monitoring and have a periodic control of the traffic sign status, we propose a fast automatic method based on video acquisition and processing that can be easily operated in patrolling vehicles provided with a camera. A pattern detection algorithm based on color and texture features is applied to the images extracted from the acquired videos in order to detect the traffic signs ROIs, which are analyzed using a statistical approach based on the Kullback-Leibler divergence and Kolmogorov-Smirnov test. Making use of a control sample of not deteriorated traffic sign images, a comparison between the acquired and the reference images is performed. Both statistical methods have been used to compare 150 pairs of traffic signs, achieving high precision and recall, proving that the proposed approach can be a good candidate solution for automatic traffic sign deterioration analysis.
... – [13]. Sabourin et al. in [14] ...
... Computing global features in sub regions of the signature image can be used to solve this problem. Methods which are based on the characterization of a small set of sub regions of the signature image have been reported in [10]– [13]. Sabourin et al. in [14] described the use of global features that are based on shape matrices for offline signature recognition. ...
This paper proposes the use of content base image retrieval (CBIR) techniques for indexing and retrieval of handwritten documents in Thai language. Issues associated with Thai handwritten documents are the lack of spacing between words, multi-level alphabets and different writing styles. This causes low recognition rate based on automated techniques such as Optical Character Recognition (OCR). This paper also examined off-line signature recognition techniques in order to adapt to Thai handwriting system for matching data. The objective of the proposal is to develop a semiautomated method to index and retrieve Thai handwritten documents based on sampled keywords by combining CBIR and signature recognition techniques.
... The most common approaches use Mahalanobis and Euclidean distances: Mahalanobis distance is used when the full covariance matrix is available for each signature class [85], [186], [188], [189], [268], [371]; Euclidean distance is considered when only the mean vector of the class is known [54], [56], [57], [273], [288], [295]. Membership functions [266] and other distance statistics [145], [310] have also been used. ...
... Along with the matching techniques, attention has been given to knowledge-base development also in relation to learning strategies [308], [310], [311] and signature modeling techniques [248], [308]. In particular, special attention has been given to writer-dependent learning strategies using only genuine specimens [156], [215], [216], [217], [328]. ...
In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.
... Goodness of fit tests can be successfully used in various areas, such as signature verification, automatic speaker identification, detection of radio frequency, economics, and data reconstruction (Biswas et al. 2008;Cho et al. 2013;Güner et al. 2009;Srinivasan et al. 2005). ...
Evaluating the distribution patterns of surface temperature data at Very Long Baseline Interferometry (VLBI)/Global Positioning System (GPS) co-located sites w.r.t. normality is one of the most important issues in modeling surface temperature data over long periods. Such evaluation can generate algorithms for filling in missing data at measurement sites. Some algorithms in the literature, such as those in the study of Cho et al. J Coast Res 65. doi: 10. 2112/SI65-321. 1, (2013), require trend, harmonic, and residual components to fill in the missing data. Trend and harmonic components estimate an optimal model that can be used to assist such algorithms when filling in missing data. The present study is based on the investigation of the normal distribution of the residuals of a surface temperature time series at VLBI/GPS co-located sites, after removing the trend and seasonal effects through harmonic components (inter-daily variations). This study uses surface temperature data collected from the VLBI/GPS co-located sites of two different regions in Europe: Matera (Italy) and Wettzell (Germany). The data collected from these sites form a time series, and time series analyses and conventional k-sigma outlier detection are implemented on these data sets before subjecting them to goodness of fit tests for normality. The residual components of the time series are acquired through a decomposing trend and signal effect from the original time series, assuming that the residuals of the time series are normally distributed. In testing the hypothesis that an observed frequency distribution fits the normal distribution, the following tests are used: Pearson χ
2, Kolmogorov-Smirnov, Anderson-Darling, Shapiro-Wilk or Shapiro-Francia, D’Agostino, Jarque-Bera, skewness, and kurtosis tests. Some graphical methods are also applied to support the results of the goodness of fit tests for normality. Some proposals on the application of the goodness of fit tests are put forward, such as the evaluation of the estimation model for trend and harmonic components by considering the properties of the implemented goodness of fit tests. The results of this study can be used to determine the optimal model for estimating trend and harmonic components. The output of the present study is expected to have an important role in modeling surface temperature distributions at co-located VLBI/GPS sites for filling in missing data. Above all, meteorological data, such as temperature, pressure, and humidity, are of specific interest for modeling tropospheric delay, the main error factor in positioning in space geodesy, which in turn makes investigations on the distribution of meteorological data more attractive in geoscience.
... Here Instance Based Learning algorithm is used in order to take the advantage of small database storage.H. Srinivasan, S.N. Srihari, M. J Beal[10]developed a signature verification system using Kolmogorov-Smirnov statistic. Here in the feature extraction stage, gradient, structural and concavity (or GSC) features are extracted. ...
signature verification is a behavioral biometric. Every day, we may face signature verification problem directly or indirectly whether it is in a banking transaction or signing a credit card transaction or authenticating a legal document. In order to solve this problem, during the last few decades, research has been going on with different approaches to introduce an efficient signature verification and identification system. This paper presents some basic concepts of signature and also explores on different approaches for verification.
... There are also many possible methods to compare the distributions. Srinivasan et al. [14] have addressed SV using Kolmogorov-Smirnov (KS) test [17] and its performance results were presented with figures. The Chi-Square test is an alternative to Anderson-Dawning (AD) and KS, goodness of fit tests. ...
This paper includes off line Signature Verification (SV) process with test results using the proposed algorithm Particle Swarm Optimization-Neural Network (PSO-NN) together with statistical analysis, Chi-square test. The verification process is performed in four steps. Signature images are scanned (data acquisition) and image processing is applied to make images suitable for extracting features (pre-processing). Each pre-processed image is then used to extract relevant geometric parameters (feature extraction) that can distinguish signatures of different volunteers. Finally, the proposed verification algorithm is tested on the database that includes 1350 skilled and genuine signatures taken from 25 volunteers. The Chi-square test is applied to see how the signature data fits with probability test function.
... Using GSC 6 approach for the feature extraction on local, medium and large scale, paper presents global, statistical, geometrical and topological features. A unique approach to verification using global features such as signature pixels gradient, statistical characteristics derived from the distribution of pixels, geometrical and topographical features are described in [9]. In contrast, a method that uses the geometric mean for the extraction of features does the verification through vertical and horizontal splitting of signature image [10]. ...
On-line handwritten signature-based personal authentication is still a challenging research topic. Although great efforts have been achieved in developing and defining a framework that systems for on-line authentication based on the handwritten signature of a person should adhere, these frameworks are still not enough because they do not include all the features that handwritten signature as a biometric feature has. In addition, there is a range of features that current system for capturing a signature posses by which it is possible to further and better define the characteristics of signatures required to be in the process of authentication of entities, but can be used not only for authentication but also for identification. The paper provides an overview of the differences between off-line and on-line mode of authentication system based on the handwritten signature as well as surveys of some properties that these features can have, supported with the latest systems to "capture" handwritten signatures.
Online signature verification methods examine the dynamics of the handwriting process to decide whether a signature is probably genuine or forged. Most of the previously proposed methods for online signature verification apply Neural Networks, Dynamic Time Warping, or Hidden Markov Model for classification and they consider several aspects, like planar coordinates, pressure, velocity, and acceleration with respect to time. Here we apply a non-parametric statistical test for a comparison of features and the verification of signatures.
Signature has been a distinguishing biometric feature. Today the human signature of a person is used as an identification of person because we are all know that the each person has distinct signature and every signature has its own physiology or behavioral characteristics. So the human signature used as a identification of person in various work like bank checks etc. The signature identification can be offline or online. We used the image processing technique for offline signature identification here no dynamic feature are available in offline identification. A brief survey on various off-line signature recognition & verification schemes is represented this paper.
A novel system for verifying signatures on Persian handwritten bank checks is presented, in this paper. The presented system includes two main phases called: training and verification phases. At first, the system is trained using some genuine signatures provided by each customer in training phase. Then verifying the signatures on incoming checks is carried out in the verification phase. Feature extraction step is conducted based on a new approach that uses Multitresolution box-counting (MRBC) method for estimating the fractal dimension of signatures. Here, signature verification is modeled as testing hypothesis, and decision about acceptance or rejection of signatures on incoming checks is carried out using Kolmogorov–Smirnov test. The presented system has been tested on two databases: our new created database and NISDCC database which was used for ICDAR 2009 signature verification competition. Our database has 1000 genuine signatures provided by 100 participants and 200 skilled forgeries copied from genuine samples by five forgers. In total our database includes 1200 Persian signatures. Obtained results show promising performance of the presented system for its application on Persian banks.
Off-line Signature Verification (SV) is performed using Particle Swarm Optimisation Neural Network (PSO NN) algorithm. The technique is based on NN approach trained with PSO algorithm. The presented verification system includes image-processing techniques and other mathematical tools in its structure. To test the performance of the proposed algorithm, three types of forgeries, namely random, unskilled and skilled, are examined. A database with 1350 skilled and genuine signatures taken from 25 volunteers is used for testing the algorithm. The experimental results are presented with comparisons on verification accuracy and statistical figures.
A new approach for verifying off-line Persian signatures is presented, in this paper. In our method, feature extraction step is conducted based on estimated Fractal Dimension (FD) of signatures images, and making decision about acceptance/rejection of test signature is formulated as testing hypothesis which is used for the first time in order to verify offline Persian signatures. The proposed method has been tested on our new created database included 1000 genuine signatures and 200 skilled forgeries which have been collected from a population of 100 human subjects with different educational background. Obtained results confirm the effectiveness of the presented method.
In this paper, we propose a model for feature selection and indexing of online signatures based person identification. For representation of online signatures, a set of 100 global features of MCYT online signature database is considered. However, MCYT based features are high dimension features which significantly increases the response time and space requirements for signature identification process. To overcome this problem, multi cluster feature selection method is proposed to reduce the dimensionality by finding a relevant feature subset. Moreover, in some applications, where the database is supposed to be very large, the identification process typically has an unacceptably long response time. A solution to speed up the identification process is to design an indexing model prior to identification which reduces the number of candidate hypotheses to be considered during matching by the identification algorithm. Hence in this paper, Kd-tree based indexing model is designed for online signatures based person identification. The experimental results reveal that the proposed model works more efficiently both in terms of time and accuracy.
The paper describes the use of Conditional Random Fields(CRF) utilizing contextual information in automati-cally labeling extracted segments of scanned documents as Machine-print, Handwriting and Noise. The result of such a labeling can serve as an indexing step for a context-based image retrieval system or a bio-metric signature verification system. A simple region growing algorithm is first used to segment the document into a number of patches. A label for each such segmented patch is inferred using a CRF model. The model is flexible enough to include signatures as a type of handwriting and isolate it from machine-print and noise. The robustness of the model is due to the inherent nature of modeling neighboring spatial dependencies in the labels as well as the observed data using CRF. Maximum pseudo-likelihood estimates for the parameters of the CRF model are learnt using conjugate gradient descent. Inference of labels is done by computing the probability of the labels under the model with Gibbs sampling. Experimental results show that this approach provides for 95.75% of the data being assigned correct labels. The CRF based model is shown to be superior to Neural Networks and Naive Bayes.
Biometric is an authentication system that identifies a person depending on his physiological or behavioral traits. Signature verification is a socially accepted biometric method and is widely used for banking transactions. In this paper, we propose Cross-validation for Graph Matching based Offline Signature Verification (CGMOSV) algorithm. Database signatures are preprocessed in which signature extraction method is used to obtain high resolution for smaller normalization box. The dissimilarity measure between two signatures in the database is determined by (i) constructing a bipartite graph G, (ii) obtaining complete matching in G and (iii) finding minimum Euclidean distance by Hungarian method. We use Cross-validation principle to select reference signatures from which an optimum decision threshold value is determined. The given test signature is pre-processed and a test feature is extracted from it, which is then compared with the threshold value to authenticate the test signature. It is observed that our algorithm gives better Equal Error Rate (EER) for skilled forgeries and random forgeries compared to the existing algorithm.
Offline signature recognition is an important form of biometric identification that can be used for various purposes. Similar to other biometric measures, signatures have inherent variability and so pose a difficult recognition problem. In this paper we explore a novel approach for reducing the variability associated with matching signatures based on curve warping. Existing techniques, such as the dynamic time warping approach, address this problem by minimizing a cost function through dynamic programming. This is by nature a one dimensional optimization process that is possible when a one dimensional parametrization of the curves is known. In this paper we propose a novel approach for solving the curve correspondence problem that is not limited by the requirement of one dimensional parametrization. The proposed approach utilizes particle dynamics and minimizes a cost function through an iterative solution of a system of first order ordinary differential equations. The proposed approach is therefore capable of handling complex curves for which a simple parametrization is not available. The proposed approach is evaluated by measuring the precision and recall rates of documents based on signature similarity. To facilitate a realistic evaluation, the signature data we use was collected from real world documents spanning a period of several decades.
Offline signature recognition is an important form of biometric identification that can be used for various purposes. Similar to other biometric measures, signatures have inherent variability and so pose a difficult recognition problem. In this paper, we explore a novel approach for reducing the variability associated with matching signatures based on curve warping. Existing techniques, such as the dynamic time warping approach, address this problem by minimizing a cost function through dynamic programming. This is by nature a 1-D optimization process that is possible when a 1-D parametrization of the curves is known. In this paper, we propose a novel approach for solving the curve correspondence problem that is not limited by the requirement of 1-D parametrization. The proposed approach utilizes particle dynamics and minimizes a cost function through an iterative solution of a system of first-order ordinary differential equations. The proposed approach is, therefore, capable of handling complex curves for which a simple parametrization is not available. The proposed approach is evaluated by measuring the precision and recall rates of documents based on signature similarity. To facilitate a realistic evaluation, the signature data we use were collected from real-world documents, spanning a period of several decades.
We present a method for the classification of 2-D partial shapes using Fourier descriptors. We formulate the problem as one of estimating the Fourier descriptors of the unknown complete shape from the observations derived from an arbitrarily rotated and scaled shape with missing segments. The method used for obtaining the estimates of the Fourier descriptors minimizes a sum of two terms; the first term of which is a least square fit to the given data subject to the condition that the number of missing boundary points is not known and the second term is the perimeter2/area of the unknown shape. Experiments with synthetic and real boundaries show that estimates closer to the true values of Fourier descriptors of complete boundaries are obtained. Also, classification experiments performed using real boundaries indicate that reasonable classification accuracies are obtained even when 20-30 percent of the data is missing.
We describe novel methods of feature extraction for recognition of single isolated character images. Our approach is flexible in that the same algorithms can be used, without modification, for feature extraction in a variety of OCR problems. These include handwritten, machine-print, grayscale, binary and low-resolution character recognition. We use the gradient representation as the basis for extraction of low-level, structural and stroke-type features. These algorithms require a few simple arithmetic operations per image pixel which makes them suitable for real-time applications. A description of the algorithms and experiments with several data sets are presented in this paper. Experimental results using artificial neural networks are presented. Our results demonstrate high performance of these features when tested on data sets distinct from the training data.
There are inevitable variations in the signature patterns written by the same person. The variations can occur in the shape or in the relative positions of the characteristic features. In this paper, two methods are proposed to track the variations. Given the set of training signature samples, the first method measures the positional variations of the one-dimensional projection profiles of the signature patterns; and the second method determines the variations in relative stroke positions in the two-dimension signature patterns. The statistics on these variations are determined from the training set. Given a signature to be verified, the positional displacements are determined and the authenticity is decided based on the statistics of the training samples. For the purpose of comparison, two existing methods proposed by other researchers were implemented and tested on the same database. Furthermore, two volunteers were recruited to perform the same verification task. Results show that the proposed system compares favorably with other methods and outperforms the volunteers.
Several dissimilarity measures for binary vectors are formulated and examined for their recognition capability in handwriting identification for which the binary micro-features are used to characterize handwritten character shapes. Pertaining to eight dissimilarity measures, i.e., Jaccard-Needham, Dice, Correlation, Yule, Russell-Rao, Sokal-Michener, Rogers-Tanmoto and Kulzinsky, the discriminary power of ten individual characters and their combination is exhaustively studied. Conclusions are made on how to choose a dissimilarity measure and how to combine hybrid features.
This paper describes a novel approach for signature verication and identication in an oine environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction. These features when used at the word level, instead of the character level, yield promising results with accuracies as high as 78% and 93% for verication and identication, respectively. This method was successfully employed in our previous theory of individuality of handwriting devel- oped at CEDAR | based on obtaining within and between writer statistical distance distributions. In this paper, exploring signature verication and identication as oine handwriting verication and identication tasks respectively, we depict a mapping from the handwriting domain to the signature domain.
Motivated by several rulings in United States courts concerning expert testimony in general, and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individual. Handwriting samples of 1,500 individuals, representative of the U.S. population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by forensic document examiners (FDEs), were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the FDE.
Learning strategies and classification methods for verification of signatures from scanned documents are proposed and evaluated. Learning strategies considered are writer independent- those that learn from a set of signature sample (including forgeries) prior to enrollment of a writer, and writer dependent- those that learn only from a newly enrolled individual. Classification methods considered include two distance based methods (one based on a threshold, which is the standard method of signature verification and biometrics, and the other based on a distance probability distribution), a Nave Bayes (NB) classifier based on pairs of feature bit values and a support vector machine (SVM). Two scenarios are considered for the writer dependent scenario: (i) without forgeries (one-class problem) and (ii) with forgery samples being available (two class problem). The features used to characterize a signature capture local geometry, stroke and topology information in the form of a binary vector. In the one-class scenario distance methods are superior while in the two-class SVM based method outperforms the other methods.
Progress on the problem of signature verification has advanced more rapidly in online applications than offline applications, in part because information which can easily be recorded in online environments, such as pen position and velocity, is lost in static offline data. In offline applications, valuable information which can be used to discriminate between genuine and forged signatures is embedded at the stroke level. We present an approach to segmenting strokes into stylistically meaningful segments and establish a local correspondence between a questioned signature and a reference signature to enable the analysis and comparison of stroke features. Questioned signatures which do not conform to the reference signature are identified as random forgeries. Most simple forgeries can also be identified, as they do not conform to the reference signature's invariant properties such as connections between letters. Since we have access to both local and global information, our approach also shows promise for extension to the identification of skilled forgeries
On-line and offline handwriting recognition: A comprehensive sur-vey
- R Plamondon
- G Lorette
R.Plamondon and G.Lorette. On-line and offline handwriting recognition: A comprehensive sur-vey. IEEE Transactions on Pattern REcognition and Machine Intelligence, 22(1):63–84, 2000.