On-line signature verification involves the use of many different features or domains. The most stable domains for a signer are analysed in this paper. For this purpose, stable domains are calculated with the weighted Direct Matching Points (ωDMP), which is a relaxed version of the classical DMP technique. In addition to the direct coupling, ωDMP also considers the information contained in the 1:N couplings from the Dynamic Time Warping algorithm. Using the ωDMP technique, state-of-the-art verification results are obtained, showing the capacity to outperform previous DMP techniques to calculate the local stability model of signers.

This article propose a complete framework to recover the dynamic properties (i.e. velocity and pressure) of an on-line Western signature from an image-based signature. The framework is based on classical approaches to recover the writing order of the strokes and a novel process to recover the kinematic properties from thinned trajectories. In order to evaluate the quality of the recovered signatures and the impact of each stage of our framework, the performance of a signature verification system on obtained signatures in each stage are compared to the performance with real signatures. As a proof of concepts, in this study we use the first 50 users of BiosecurID signature database since they contain both the on-line and off-line version of Western signatures.

What can be done with only one enrolled real hand-written signature in Automatic Signature Verification (ASV)? Using 5 or 10 signatures for training is the most common case to evaluate ASV. In the scarcely addressed case of only one available signature for training, we propose to use modified duplicates. Our novel technique relies on a fully neuromuscular representation of the signatures based on the Kinematic Theory of rapid human movements and its Sigma-Lognormal model. This way, a real on-line signature is converted into the Sigma-Lognormal model domain. The model parameters are then varied to generate new duplicated signatures.

In the field of automatic signature verification, a major challenge for statistical analysis and pattern recognition is the small number of reference signatures per user. Score normalization, in particular, is challenged by the lack of information about intra-user variability. In this paper, we analyze several approaches to score normalization for dynamic time warping and propose a new two-stage normalization which detects simple forgeries in a first stage and copes with more skilled forgeries in a second stage. An experimental evaluation is conducted on two data sets with different characteristics, namely the MCYT online signature corpus, which contains over three hundred users, and the SUSIG visual sub-corpus, which contains highly skilled forgeries. The results demonstrate that score normalization is a key component for signature verification and that the proposed two-stage normalization achieves some of the best results on these difficult data sets both for random and for skilled forgeries.

In the networked society, in which a multitude of different devices can be used for signature acquisition, specific research is still needed to determine the extent to which features of an input signature depend on the characteristics of the signature acquisition process. In this paper an experimental investigation is carried out on constrained signatures, which were acquired using writing boxes with different areas and shapes. The paper discusses different behaviour of dynamic and static features with respect to the writing boxes.

Script identification is an important step in multi-script document analysis. As different textures present in text portion of a script are the main distinct features of the script, in this paper, we proposed a new algorithm for printed script identification based on texture analysis. Since local patterns is a unifying concept for traditional statistical and structural approaches of texture analysis, here the basic idea is to use the histogram of the local patterns as description of the script stroke directions distribution which is the characteristic of every script. As local pattern, the basic version of the Local Binary Patterns (LBP) and a modified version of the Orientation of the Local Binary Patterns (OLBP) are proposed. A Least Square Support Vector Machine (LS-SVM) is used as identifier. The scheme has been verified on two databases. The first or training database is a database with 200 sheets of 10 different scripts. The scripts font is provided by the Google translator. The second or test database has been obtained by scanning different newspapers and books. It contains 5 common scripts among 10 different scripts of the first database. From the experiment we obtained encouraging results.

The automatic handwritten signature verification is an open problem for the scientific community. The most of the published studies examine a generic document trying to locate where the signature has been written, to segment the signature removing complex backgrounds containing lines and letter and to determining whether the signature was made by the owner. However, there are no studies to determine automatically the author of a fake. This paper presents a first approach to the identification of a static signature forger. The underlying hypothesis is the fact that a forger finds difficult to fight against their own free natural way of writing, leading to the second hypothesis that under several conditions it is possible to isolate these features to determine a fake within a population of known forgers. The experiments shown that gray level based features are a good start point to detect who has written the signatures.

Features representing information about pressure distribution from a static image of a handwritten signature are analyzed for an offline verification system. From gray-scale images, its histogram is calculated and used as "spectrum'' for calculation of pseudo-cepstral coefficients. Finally, the unique minimum-phase sequence is estimated and used as feature vector for signature verification. The optimal number of pseudo-coefficients is estimated for best system performance. Experiments were carried out using a database containing signatures from 100 individuals. The robustness of the analyzed system for simple forgeries is tested out with a LS-SVM model. For the sake of completeness, a comparison of the results obtained by the proposed approach with similar works published using pseudo-dynamic feature for offline signature verification is presented.

The current need for large databases to evaluate automatic biometric recognition systems has motivated the developing of the GPDS-960 corpus, an off-line handwritten signature database which contains 24 genuine signatures and 30 forgeries of 960 individuals. This paper describes the GPDS signature corpus, gives details about the acquisition protocols and presents preliminary verification results obtained using the GPDS data.

The effect of changing the image resolution over an off-line signature verification system performance is analyzed. The geometrical features used for the system analyzed in this paper are based on two vectors which represent the envelope and the interior stroke distribution in polar and Cartesian coordinates. Image resolution is progressively diminished from an initial 600 ppp resolution till 45 ppp. The robustness of the analyzed system for random and simple forgeries is tested out with a hidden Markov model. The results show that 150 ppp offers a good trade-off between performance and image resolution for static features.