In this study, several feature combinations are studied to analyse their relevance for online signature verification. Different time functions associated with the signing process are analysed in order to provide some insight on their actual discriminative power. This analysis could also help forensic handwriting experts (FHEs) to further understand the signatures and the writer's behaviour. Among the different feature combinations analysed, a set of features which seems to be relevant for signature analysis by FHEs is particularly considered. The feasibility of developing a system which could complement the FHEs work is evaluated. Two different approximations of the analysed time functions are proposed, one based on the Legendre polynomials and another based on the wavelet decomposition. The coefficients in these orthogonal series expansions of the time functions are used as features to model them. Two different signature styles are considered, namely, Western and Chinese, of one of the most recent publicly available signature databases. The experimental results are promising, in particular for the features that seem to be relevant for the FHEs, since the obtained verification error rates are comparable with the ones reported in the state-of-the-art over the same datasets.

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.

In this study, the authors present a novel dissimilarity-based signature modelling framework for writer-independent off-line signature verification. The proposed framework utilises a discrete Radon transform and a dynamic time warping algorithm for writer-independent signature representation in dissimilarity space, and a writer-specific strategy for dissimilarity normalisation. A discriminative classifier, either a discriminant function or a support vector machine, is utilised for verification purposes. Both linear and non-linear decision boundaries are considered. The authors show that the novel techniques presented in this study provide an improved platform for writer-independent signature modelling. When evaluated on Dolfing's data set, a signature database that contains 1530 genuine signatures and 3000 amateur skilled forgeries, the systems presented in this study outperform all previous systems also evaluated on this data set.

Standard signature verification (SV) systems are writer-dependent (WD), where a specific classifier is designed for each individual. It is inconvenient to ask a user to provide enough number of signature samples to design his WD classifier. In practice, very few samples are collected and inaccurate classifiers maybe produced. To overcome this, writer-independent (WI) systems are introduced. A global classifier is designed using a development database, prior to enrolling users to the system. For these systems, signature templates are needed for verification, and the template databases can be compromised. Moreover, state-of-the-art WI and WD systems provide enhanced accuracy through information fusion at either feature, score or decision levels, but they increase computational complexity. In this study, a hybrid WI–WD system is proposed, as a compromise of the two approaches. When a user is enrolled to the system, a WI classifier is used to verify his queries. During operation, user samples are collected and adapt the WI classifier to his signatures. Once adapted, the resulting WD classifier replaces the WI classifier for this user. Simulations on the Brazilian and the GPDS signature databases indicate that the proposed hybrid system provides comparative accuracy as complex WI and WD systems, while decreases the classification complexity.

Handwritten signature is one of the oldest biometric attributes used for authentication of an individual or a document. The purpose of this study is to present an empirical contribution towards the understanding of signature verification using a novel method involving off-line Hindi (Devnagari) signatures. Although research in the field of signature verification involving Western signatures has been well studied, there has been relatively little attention devoted to non-Western signatures such as Chinese, Japanese, Arabic, Persian etc. In this study, the performance of an off-line signature verification system involving Hindi signatures, whose style is distinct from Western scripts, was investigated. The gradient feature, Zernike moment features and SVMs were considered for verification. To the best of the authors’ knowledge, Hindi signatures investigated as part of a large dataset have never been used for the task of signature verification, and this research work is only the second important report using Hindi signatures in this area. An encouraging accuracy of 90.69% was obtained using gradient feature. The Hindi signature database employed for experimentation consisted of 2400 (100 × 24) genuine signatures and 3000 (100 × 30) forgeries. The error rates of 11.50% FRR and 7.12% FAR were obtained through experimentation using gradient features.

With the explosive growth of the tablet form factor and greater availability of pen-based direct input, online writer identification is increasingly becoming critical for person identification, digital forensics as well as downstream applications such as intelligent and adaptive user environments, search, indexing and retrieval of handwritten documents. Extant research has approached writer identification by using writing styles as a discriminative function between writers. In contrast, the authors model writing styles as a shared component of an individual's handwriting. They develop a theoretical framework for this conceptualisation and model it by using a three-level hierarchical Bayesian model (Latent Dirichlet Allocation). In this text-independent, unsupervised model each writer's handwriting is modelled as a distribution over finite writing styles that are shared among writers. They test their model on a new online handwriting dataset IBM_UB_1 and also offer benchmark comparisons by using the IAM-OnDB database. Their experiments show comparable results to the current benchmarks and demonstrate the efficacy of explicitly modelling the shared writing styles.

Writer verification methods from handwriting images have been widely used in biometrics, forensic casework and so on. However, their performance conducted by a computer is still far behind that of human beings. This study proposes an offline writer verification method which uses a new hybrid feature. The characteristics of this new feature are the combination of shape and pen pressure information. For shape information, the authors use the weighted direction code histogram, which is often used in Japanese handwriting recognition and writer verification methods. For pen pressure information, the authors use the texture features of infrared (IR) images obtained from a multi-band image scanner. Although the simple use of pen pressure information encoded in the IR image cannot improve the geometric mean (g-mean)-based error rate, the use of clipping process and second-order statistics can decrease the g-mean-based error rate from 4.8 to 3.2%.

Writer identification is a complex task as the handwriting of an individual encapsulates lot of information pertaining to text and personality of a writer. To learn a model to distinguish one writer from the other, it is important to capture every nuance of the handwriting of an individual. Learning such model poses two challenges. First, discriminatory variables maybe large and potentially related leading to a complex discriminatory function. Second, it will require large amount of training data to learn a complex and possibly high-dimensional function. In this study, the authors are proposing a semi-supervised framework for writer identification for offline handwritten documents that leverages the information hidden in the unlabelled samples. Proposed framework models the complexity of approximating the optimal hypothesis by breaking the main task into several subtasks and learning a separate hypothesis for each subtask. All the hypotheses pertaining to the subtasks will be used for the best model selection by retrieving a common substructure that has high correspondence with all the candidate hypotheses. The obtained substructure acts as a knowledge base that has the contextual information, which is otherwise difficult to retrieve. The extra information can be used to improve the performance of the identification model.