Donato Impedovo

Automatic Signature Verification: The State of the Art

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.

Zoning methods for handwritten character recognition

This paper presents a survey on zoning methods for handwritten character recognition. Through the analysis of the relevant literature in the field, the most valuable zoning methods are presented in terms of both topologies and membership functions. Throughout the paper, diverse zoning topologies are presented based on both static and adaptive approaches. Concerning static approaches, uniform and non-uniform zoning strategies are discussed. When adaptive zonings are considered, manual and automatic strategies for optimal zoning design are illustrated as well as the most appropriate zoning representation techniques. In addition, the role of membership functions for zoning-based classification is highlighted and the diverse approaches to membership function selection are presented. Concerning global membership functions, the paper introduces order-based approaches as well as fuzzy approaches using border-based and ranked-based fuzzy membership values. Concerning local membership functions, the recent parameter-based approaches are described, in which the optimal membership-function is selected for each zone of the zoning method. Finally, a comparative analysis on the performance of zoning methods is presented and the most interesting approaches are focused on in terms of topology design and membership function selection. A list of selected references is provided as a useful tool for interested researchers working in the field. A survey on zoning methods for handwritten character recognition is presented.Static zoning topologies and approaches are discussed.Adaptive optimal zonings approaches are discussed.Global and Local Membership function are introduced and described.A comparative analysis on the performance of different zoning methods is reported.

Handwritten Signature Verification: New Advancements and Open Issues

Recently, research in handwritten signature verification has been considered with renewed interest. In fact, in the age of e-society, handwritten signature still represents an extraordinary means for personal verification and the possibility of using automatic signature verification in a range of applications is becoming a reality. This paper focuses on some of the most remarkable aspects the field and highlights some recent research directions. A list of selected publications is also provided for interested researchers.

Verification of Static Signatures by Optical Flow Analysis

A new approach for static signature verification is presented in this paper. The approach uses optical flow to estimate local stability among signatures. In the enrollment stage, optical flow is used to define a stability model of the genuine signatures for each signer. In the verification stage, the stability between the unknown signature and each one of the reference signatures is estimated and consistency with the stability model of the signer is evaluated. The experimental results, carried out on the signatures in the GPDS database, demonstrate the effectiveness of the new approach.

Adaptive Membership Functions for Handwritten Character Recognition by Voronoi-Based Image Zoning

In the field of handwritten character recognition, image zoning is a widespread technique for feature extraction since it is rightly considered to be able to cope with handwritten pattern variability. As a matter of fact, the problem of zoning design has attracted many researchers who have proposed several image-zoning topologies, according to static and dynamic strategies. Unfortunately, little attention has been paid so far to the role of feature-zone membership functions that define the way in which a feature influences different zones of the zoning method. The result is that the membership functions defined to date follow nonadaptive, global approaches that are unable to model local information on feature distributions. In this paper, a new class of zone-based membership functions with adaptive capabilities is introduced and its effectiveness is shown. The basic idea is to select, for each zone of the zoning method, the membership function best suited to exploit the characteristics of the feature distribution of that zone. In addition, a genetic algorithm is proposed to determine—in a unique process—the most favorable membership functions along with the optimal zoning topology, described by Voronoi tessellation. The experimental tests show the superiority of the new technique with respect to traditional zoning methods.

The Influence of Frame Length on Speaker Identification Performance

Speaker recognition/identification is a challenge for the implementation of security applications. Unfortunately, degradation in performance is usually observed for high pitched speakers and also whenever average pitch varies significantly between enrolment and testing. In this paper, a study on the impact of the frame length used to extract features from speech signal on the performance of speaker identification is presented. Tests have been carried out on a text-dependent database. Results show that a combination of different frame sizes between the training and the recognition phases can cope with the degradation. A reduction between 40% and 65% in false rejections has been generally observed.

Analysis of Stability in Static Signatures Using Cosine Similarity

This paper presents a new technique for the analysis of stability in static signature images. The technique uses an equimass segmentation approach to non-uniformly split signatures into a standard number of regions. Successively, a multiple matching technique is adopted to estimate stability of each region, based on cosine similarity. The GPDS database has been considered for the experimental test. The results demonstrate the validity of the novel approach and highlight some directions for further research.

Cosine similarity for analysis and verification of static signatures

The stability of handwritten signatures is a crucial characteristic for both investigating the nature of the signature apposition process and improving systems for automatic signature verification. In this study, a new technique for the analysis of stability in static signature images is discussed. The technique adopts a feature-based strategy to derive regional information from a static signature image and uses cosine similarity to estimate the degree of regional stability among genuine signatures, according to a multiple matching strategy. The experimental test carried out using signatures in the Grupo de Procesado Digital de Senales (GPDS) database has demonstrated the validity of this novel approach in obtaining stability information and deriving significant signer-independent and signer-dependent properties of the signing process, useful for verification aims.

On-line Signature Verification by Stroke-Dependent Representation Domains

In this paper a new system for dynamic signature verification is presented. It is based on the consideration that each region of an handwritten signature can convey personal characteristics in diverse domains. Therefore, a multi-expert approach is considered in which each stroke of the signature is evaluated in the most profitable domain of representation. The experimental results demonstrate the effectiveness of the proposed approach.

Multidomain Verification of Dynamic Signatures Using Local Stability Analysis

This paper presents a new approach for online signature verification that exploits the potential of local stability information in handwritten signatures. Different from previous models, this approach classifies a signature using a multidomain strategy. A signature is first split into different segments based on the stability model of a signer. Then, according to the stability model, for each segment, the most profitable domain of representation for verification purposes is detected. In the verification stage, the authenticity of each segment of the unknown signature is evaluated in the most profitable domain of representation. The authenticity of the unknown signature is then determined by combining local verification decisions. The study was carried out on the signatures in the SUSIG database, and the experimental results, thus, obtained confirm the effectiveness of the proposed approach, when compared with others in the literature.