Online Signature Verification using SVD Method

Online signature verification rests on hypothesis which any writer has similarity among signature samples, with scale variability and small distortion. This is a dynamic method in which users sign and then biometric system recognizes the signature by analyzing its characters such as acceleration, pressure, and orientation. The proposed technique for online signature verification is based on the Singular Value Decomposition (SVD) technique which involves four aspects: I) data acquisition and preprocessing 2) feature extraction 3) matching (classification), 4) decision making. The SVD is used to find r-singular vectors sensing the maximal energy of the signature data matrix A, called principle subspace thus account for most of the variation in the original data. Having modeled the signature through its r-th principal subspace, the authenticity of the tried signature can be determined by calculating the average distance between its principal subspace and the template signature. The input device used for this signature verification system is 5DT Data Glove 14 Ultra which is originally design for virtual reality application. The output of the data glove, which captures the dynamic process in the signing action, is the data matrix, A to be processed for feature extraction and matching. This work is divided into two parts. In part I, we investigate the performance of the SVD-based signature verification system using a new matching technique, that is, by calculating the average distance between the different subspaces. In part IJ, we investigate the performance of the signature verification with reducedsensor data glove. To select the 7-most prominent sensors of the data glove, we calculate the F-value for each sensor and choose 7 sensors that gives the highest Fvalue

Online Signature Verification using SVD Method

Online signature verification rests on hypothesis which any writer has similarity among signature samples, with scale variability and small distortion. This is a dynamic method in which users sign and then biometric system recognizes the signature by analyzing its characters such as acceleration, pressure, and orientation. The proposed technique for online signature verification is based on the Singular Value Decomposition (SVD) technique which involves four aspects: I) data acquisition and preprocessing 2) feature extraction 3) matching (classification), 4) decision making. The SVD is used to find r-singular vectors sensing the maximal energy of the signature data matrix A, called principle subspace thus account for most of the variation in the original data. Having modeled the signature through its r-th principal subspace, the authenticity of the tried signature can be determined by calculating the average distance between its principal subspace and the template signature. The input device used for this signature verification system is 5DT Data Glove 14 Ultra which is originally design for virtual reality application. The output of the data glove, which captures the dynamic process in the signing action, is the data matrix, A to be processed for feature extraction and matching. This work is divided into two parts. In part I, we investigate the performance of the SVD-based signature verification system using a new matching technique, that is, by calculating the average distance between the different subspaces. In part IJ, we investigate the performance of the signature verification with reducedsensor data glove. To select the 7-most prominent sensors of the data glove, we calculate the F-value for each sensor and choose 7 sensors that gives the highest Fvalue

Online Signature Verification: Improving Performance through Pre-classification Based on Global Features

In this paper, a pre-classification stage based on global features is incorporated to an online signature verification system for the purposes of improving its performance. The pre-classifier makes use of the discriminative power of some global features to discard (by declaring them as forgeries) those signatures for which the associated global feature is far away from its respective mean. For the remaining signatures, features based on a wavelet approximation of the time functions associated with the signing process, are extracted, and a Random Forest based classification is performed. The experimental results show that the proposed pre-classification approach, when based on the apppropriate global feature, is capable of getting error rate improvements with respect to the case where no pre-classification is performed. The approach also has the advantages of simplifying and speeding up the verification process.Fil: Parodi, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Gómez, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentin

Feature Representation for Online Signature Verification

Biometrics systems have been used in a wide range of applications and have improved people authentication. Signature verification is one of the most common biometric methods with techniques that employ various specifications of a signature. Recently, deep learning has achieved great success in many fields, such as image, sounds and text processing. In this paper, deep learning method has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information Forensics and Securit

Visual identification by signature tracking

We propose a new camera-based biometric: visual signature identification. We discuss the importance of the parameterization of the signatures in order to achieve good classification results, independently of variations in the position of the camera with respect to the writing surface. We show that affine arc-length parameterization performs better than conventional time and Euclidean arc-length ones. We find that the system verification performance is better than 4 percent error on skilled forgeries and 1 percent error on random forgeries, and that its recognition performance is better than 1 percent error rate, comparable to the best camera-based biometrics

Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers

We present a new approach for online handwritten signature classification and verification based on descriptors stemming from Information Theory. The proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher Information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results produced surpass state-of-the-art techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.Comment: Submitted to PLOS On