Multiple Classifier System for Writer Independent Offline Handwritten Signature Verification using Hybrid Features (original) (raw)
Related papers
2018
An efficient multiple classifier system for writer independent offline handwritten signature verification is proposed. Local oriented statistical information booster and histogram of oriented gradients feature descriptors are extracted from the signature image and a genetic algorithm is used to reduce the dimension of feature descriptors. To create multiple classifier system, two scenarios are generated. In scenario I, training set is divided into subsets using k-fold cross validation method and these training subsets are used to train the classifiers of multiple classifier system using same training algorithm for all classifiers. In scenario II, the classifiers of multiple classifier system are trained with multiple training algorithms. Unskilled and skilled forgeries are used to test the performance of multiple classifier systems. The performance analysis is carried out using support vector machine with polynomial kernel, quadratic kernel and gaussian radial basis function kernel....
2018
Various offline handwritten signature verification systems using writer independent approach are proposed by the researchers in last few years using numerous perspectives, like feature extraction techniques, feature selection techniques, classifiers used to develop the system etc. Despite the progressions in this framework, building classifier that can isolate the genuine and skilled forgery signatures is still a tough task. In this work, multiple classifier system is proposed to develop the writer independent offline handwritten signature verification system. To train the classifiers of multiple classifier system, feature vectors of the training set are partitioned into subsets and classifiers are trained using these subsets to preserve the diversity. The pixels lying on the elliptical curve paths are used to extract the features from genuine and forgery signature images. Two scenarios are proposed for the performance analysis. In the first scenario, the classifiers are trained usi...
Offline Handwritten Signatures Classification Using Wavelets and Support Vector Machines
Offline Signature Classification has been extensively studied for many years. The challenge in this area is the correct classification of skilled forgeries which are the result of deliberate practice to imitate the signatures of any person. In this paper the preprocessed images of genuine handwritten signatures are subjected to analysis by Wavelet Packets. A regular wavelet like db4 has been used to do the decomposition upto four levels. The resulting decomposed signal is further subjected to wavelet multiscale principal component analysis done for ten levels. The principal components are chosen according to the kais rule. The selected principal components consist of details at ten different levels and one approximation for each signature image. For a given test signature image the principal components are extracted in the same way and the principal components at each level are compared against the mean principal components of the genuine signatures at the corresponding level and the difference is within the permissible range, then a score is assigned. The collective score obtained due to all levels is used to classify the signature as genuine or forgery. The proposed system has a FAR of 12% and a FRR of 8%.
International Journal of Computer Applications
Signature is critical for authentication and authorization in commercial, financial and legal transactions and fittingly, it is one of the most commonly used biometrics for authentication. Hence, an accurate and efficient signature verification system is required. The objective of signature verification is to discriminate the original signatures from the forged ones. It is a challenging task as even two signatures of the same person possess variations in different areas such as the starting and ending positions, the angle of inclination, relative spacing between letters, height, width etc. Offline signature verification is even more challenging as it is devoid of the dynamic information about the signing process. Although numerous research works have been done in the area of offline signature verification in last decades, it still remains an open research problem. There are three common phases in signature verification system: image preprocessing, feature extraction and verification. In this paper, two novel features have been presented that can be extracted from preprocessed signature images in the feature extraction phase. The proposed features are: i) Stroke angle and average intersected points ii) Pixel density of the signature nucleus. The goal of this research is to strengthen the feature set with the proposed features what will help to get more accurate verification of the signatures.
A Hierarchical Handwritten Offline Signature Recognition System
This paper presents an original approach for solving the problem of offline handwritten signature recognition, and a new hierarchical, data-partitioning based solution for the recognition module. Our approach tackles the problem we encountered with an earlier version of our system when we attempted to increase the number of classes in the dataset: as the complexity of the dataset increased, the recognition rate dropped unacceptably for the problem considered. The new approach employs a data partitioning strategy to generate smaller sub-problems, for which the induced classification model should attain better performance. Each sub-problem is then submitted to a learning method, to induce a classification model in a similar fashion with our initial approach. We have performed several experiments and analyzed the behavior of the system by increasing the number of instances, classes and data partitions. We continued using the Naïve Bayes classifier for generating the classification models for each data partition. Overall, the classifier performs in a hierarchical way: a top level for data partitioning via clustering and a bottom level for classification sub-model induction, via the Naïve Bayes classifier. Preliminary results indicate that this is a viable strategy for dealing with signature recognition problems having a large number of persons.
Pattern Recognition, 2015
The limited number of writers and genuine signatures constitutes the main problem for designing a robust Handwritten Signature Verification System (HSVS). We propose, in this paper, the use of One-Class Support Vector Machine (OC-SVM) based on writer-independent parameters, which takes into consideration only genuine signatures and when forgery signatures are lack as counterexamples for designing the HSVS. The OC-SVM is effective when large samples are available for providing an accurate classification. However, available handwritten signature samples are often reduced and therefore the OC-SVM generates an inaccurate training and the classification is not well performed. In order to reduce the misclassification, we propose a modification of decision function used in the OC-SVM by adjusting carefully the optimal threshold through combining different distances used into the OC-SVM kernel. Experimental results conducted on CEDAR and GPDS handwritten signature datasets show the effective use of the proposed system comparatively to the state of the art.
Classification approaches in off-line handwritten signature verification
WSEAS Transactions on Mathematics, 2009
The aim of off-line signature verification is to decide, whether a signature originates from a given signer based on the scanned image of the signature and a few images of the original signatures of the signer. Although the verification process can be thought to as a monolith component, it is recommended to divide it into loosely coupled phases (like preprocessing, feature extraction, feature matching, feature comparison and classification) allowing us to gain a better control over the precision of different components. This paper focuses on classification, the last phase in the process, covering some of the most important general approaches in the field. Each approach is evaluated for applicability in signature verification, identifying their strength and weaknesses. It is shown, that some of these weak points are common between the different approaches and can partially be eliminated with our proposed solutions. To demonstrate this, several local features are introduced and compared using different classification approaches. Results are evaluated on the database of the Signature Verification Competition 2004.
2015
The various studies conducted for classification of handwritten signatures of people have shown that the task is difficult because there is intra personal differences among the signatures of the same person. The signatures of the same person vary with time, age of the person and also because of the emotional state of a person. The task of classifying the skilled forgery signatures is all the more challenging because they are the result of lot of practice, closely imitating the signature. Neural networks based classifiers have proved to yield very accurate results. This paper for offline signature verification uses the images stored in the GPDS database. The preprocessed images are decomposed using discrete wavelet t ransform up to the maximum level. The wavelet energy features corresponding to the approximation and detail along with the approximation and detail coefficients make the feature set. A pattern recognition neural network is designed which classifies the inputs based on th...
International Journal of Computer Applications
The various studies conducted for classification of handwritten signatures of people have shown that the task is difficult because there is intra personal differences among the signatures of the same person. The signatures of the same person vary with time, age of the person and also because of the emotional state of a person. The task of classifying the skilled forgery signatures is all the more challenging because they are the result of lot of practice, closely imitating the signature. Neural networks based classifiers have proved to yield very accurate results. This paper for offline signature verification uses the images stored in the GPDS database. The preprocessed images are decomposed using discrete wavelet transform up to the maximum level. The wavelet energy features corresponding to the approximation and detail along with the approximation and detail coefficients make the feature set. A pattern recognition neural network is designed which classifies the inputs based on the...
Discriminative Features Mining for Offline Handwritten Signature Verification
3D Research, 2014
Signature verification is an active research area in the field of pattern recognition. It is employed to identify the particular person with the help of his/her signature's characteristics such as pen pressure, loops shape, speed of writing and up down motion of pen, writing speed, pen pressure, shape of loops, etc. in order to identify that person. However, in the entire process, features extraction and selection stage is of prime importance. Since several signatures have similar strokes, characteristics and sizes. Accordingly, this paper presents combination of orientation of the skeleton and gravity centre point to extract accurate pattern features of signature data in offline signature verification system. Promising results have proved the success of the integration of the two methods.