Gravitational Search For Designing A Fuzzy Rule-Based Classifiers For Handwritten Signature Verification

Handwritten signatures are used in authentication systems as a universal biometric identifier. Signature authenticity verification requires building and training a classifier. This paper describes a new approach to the verification of handwritten signatures by dynamic characteristics with a fuzzy rule-based classifier. It is suggested to use the metaheuristic Gravitational Search Algorithm for the selection of the relevant features and tuning fuzzy rule parameters. The efficiency of the approach was tested with an original dataset; the type II errors in finding the signature authenticity did not exceed 0.5% for the worst model and 0.08% for the best model

Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification

This paper introduces a novel approach to leverage the knowledge of existing expert models for training new Convolutional Neural Networks, on domains where task-specific data are limited or unavailable. The presented scheme is applied in offline handwritten signature verification (OffSV) which, akin to other biometric applications, suffers from inherent data limitations due to regulatory restrictions. The proposed Student-Teacher (S-T) configuration utilizes feature-based knowledge distillation (FKD), combining graph-based similarity for local activations with global similarity measures to supervise student's training, using only handwritten text data. Remarkably, the models trained using this technique exhibit comparable, if not superior, performance to the teacher model across three popular signature datasets. More importantly, these results are attained without employing any signatures during the feature extraction training process. This study demonstrates the efficacy of leveraging existing expert models to overcome data scarcity challenges in OffSV and potentially other related domains

Recognizing Handwriting Styles in a Historical Scanned Document Using Unsupervised Fuzzy Clustering

The forensic attribution of the handwriting in a digitized document to multiple scribes is a challenging problem of high dimensionality. Unique handwriting styles may be dissimilar in a blend of several factors including character size, stroke width, loops, ductus, slant angles, and cursive ligatures. Previous work on labeled data with Hidden Markov models, support vector machines, and semi-supervised recurrent neural networks have provided moderate to high success. In this study, we successfully detect hand shifts in a historical manuscript through fuzzy soft clustering in combination with linear principal component analysis. This advance demonstrates the successful deployment of unsupervised methods for writer attribution of historical documents and forensic document analysis.Comment: 26 pages in total, 5 figures and 2 table

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline

Content Recognition and Context Modeling for Document Analysis and Retrieval

The nature and scope of available documents are changing significantly in many areas of document analysis and retrieval as complex, heterogeneous collections become accessible to virtually everyone via the web. The increasing level of diversity presents a great challenge for document image content categorization, indexing, and retrieval. Meanwhile, the processing of documents with unconstrained layouts and complex formatting often requires effective leveraging of broad contextual knowledge. In this dissertation, we first present a novel approach for document image content categorization, using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant local shape feature that is generic enough to be detected repeatably and is segmentation free. A concise, structurally indexed shape lexicon is learned by clustering and partitioning feature types through graph cuts. Our idea finds successful application in several challenging tasks, including content recognition of diverse web images and language identification on documents composed of mixed machine printed text and handwriting. Second, we address two fundamental problems in signature-based document image retrieval. Facing continually increasing volumes of documents, detecting and recognizing unique, evidentiary visual entities (\eg, signatures and logos) provides a practical and reliable supplement to the OCR recognition of printed text. We propose a novel multi-scale framework to detect and segment signatures jointly from document images, based on the structural saliency under a signature production model. We formulate the problem of signature retrieval in the unconstrained setting of geometry-invariant deformable shape matching and demonstrate state-of-the-art performance in signature matching and verification. Third, we present a model-based approach for extracting relevant named entities from unstructured documents. In a wide range of applications that require structured information from diverse, unstructured document images, processing OCR text does not give satisfactory results due to the absence of linguistic context. Our approach enables learning of inference rules collectively based on contextual information from both page layout and text features. Finally, we demonstrate the importance of mining general web user behavior data for improving document ranking and other web search experience. The context of web user activities reveals their preferences and intents, and we emphasize the analysis of individual user sessions for creating aggregate models. We introduce a novel algorithm for estimating web page and web site importance, and discuss its theoretical foundation based on an intentional surfer model. We demonstrate that our approach significantly improves large-scale document retrieval performance

Writer Identification for chinese handwriting

Abstract Chinese handwriting identification has become a hot research in pattern recognition and image processing. In this paper, we present overview of relevant papers from the previous related studies until to the recent publications regarding to the Chinese Handwriting Identification. The strength, weaknesses, accurateness and comparison of well known approaches are reviewed, summarized and documented. This paper provides broad spectrum of pattern recognition technology in assisting writer identification tasks, which are at the forefront of forensic and biometrics based on identification application

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

A New Swarm-Based Framework for Handwritten Authorship Identification in Forensic Document Analysis

Feature selection has become the focus of research area for a long time due to immense consumption of high-dimensional data. Originally, the purpose of feature selection is to select the minimally sized subset of features class distribution which is as close as possible to original class distribution. However in this chapter, feature selection is used to obtain the unique individual significant features which are proven very important in handwriting analysis of Writer Identification domain. Writer Identification is one of the areas in pattern recognition that have created a center of attention by many researchers to work in due to the extensive exchange of paper documents. Its principal point is in forensics and biometric application as such the writing style can be used as bio-metric features for authenticating the identity of a writer. Handwriting style is a personal to individual and it is implicitly represented by unique individual significant features that are hidden in individual’s handwriting. These unique features can be used to identify the handwritten authorship accordingly. The use of feature selection as one of the important machine learning task is often disregarded in Writer Identification domain, with only a handful of studies implemented feature selection phase. The key concern in Writer Identification is in acquiring the features reflecting the author of handwriting. Thus, it is an open question whether the extracted features are optimal or near-optimal to identify the author. Therefore, feature extraction and selection of the unique individual significant features are very important in order to identify the writer, moreover to improve the classification accuracy. It relates to invarianceness of authorship where invarianceness between features for intra-class (same writer) is lower than inter-class (different writer). Many researches have been done to develop algorithms for extracting good features that can reflect the authorship with good performance. This chapter instead focuses on identifying the unique individual significant features of word shape by using feature selection method prior the identification task. In this chapter, feature selection is explored in order to find the most unique individual significant features which are the unique features of individual’s writing. This chapter focuses on the integration of Swarm Optimized and Computationally Inexpensive Floating Selection (SOCIFS) feature selection technique into the proposed hybrid of Writer Identification framework 386 S.F. Pratama et al. and feature selection framework, namely Cheap Computational Cost Class-Specific Swarm Sequential Selection (C4S4). Experiments conducted to proof the validity and feasibility of the proposed framework using dataset from IAM Database by comparing the proposed framework to the existing Writer Identification framework and various feature selection techniques and frameworks yield satisfactory results. The results show the proposed framework produces the best result with 99.35% classification accuracy. The promising outcomes are opening the gate to future explorations in Writer Identification domain specifically and other domains generally

Multimodal biometrics scheme based on discretized eigen feature fusion for identical twins identification

The subject of twins multimodal biometrics identification (TMBI) has consistently been an interesting and also a valuable area of study. Considering high dependency and acceptance, TMBI greatly contributes to the domain of twins identification in biometrics traits. The variation of features resulting from the process of multimodal biometrics feature extraction determines the distinctive characteristics possessed by a twin. However, these features are deemed as inessential as they cause the increase in the search space size and also the difficulty in the generalization process. In this regard, the key challenge is to single out features that are deemed most salient with the ability to accurately recognize the twins using multimodal biometrics. In identification of twins, effective designs of methodology and fusion process are important in assuring its success. These processes could be used in the management and integration of vital information including highly selective biometrics characteristic possessed by any of the twins. In the multimodal biometrics twins identification domain, exemplification of the best features from multiple traits of twins and biometrics fusion process remain to be completely resolved. This research attempts to design a new scheme and more effective multimodal biometrics twins identification by introducing the Dis-Eigen feature-based fusion with the capacity in generating a uni-representation and distinctive features of numerous modalities of twins. First, Aspect United Moment Invariant (AUMI) was used as global feature in the extraction of features obtained from the twins handwritingfingerprint shape and style. Then, the feature-based fusion was examined in terms of its generalization. Next, to achieve better classification accuracy, the Dis-Eigen feature-based fusion algorithm was used. A total of eight distinctive classifiers were used in executing four different training and testing of environment settings. Accordingly, the most salient features of Dis-Eigen feature-based fusion were trained and tested to determine the accuracy of the classification, particularly in terms of performance. The results show that the identification of twins improved as the error of similarity for intra-class decreased while at the same time, the error of similarity for inter-class increased. Hence, with the application of diverse classifiers, the identification rate was improved reaching more than 93%. It can be concluded from the experimental outcomes that the proposed method using Receiver Operation Characteristics (ROC) considerably increases the twins handwriting-fingerprint identification process with 90.25% rate of identification when False Acceptance Rate (FAR) is at 0.01%. It is also indicated that 93.15% identification rate is achieved when FAR is at 0.5% and 98.69% when FAR is at 1.00%. The new proposed solution gives a promising alternative to twins identification application

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study