Offline Handwritten Signature Verification - Literature Review <s> Datasets <s> This paper describes a novel approach for signature verification and identification in an offline environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction. These features when used at the word level, instead of the character level, yield promising results with accuracies as high as 78% and 93% for verification and identification, respectively. This method was successfully employed in our previous theory of individuality of handwriting developed at CEDAR — based on obtaining within and between writer statistical distance distributions. In this paper, exploring signature verification and identification as offline handwriting verification and identification tasks respectively, we depict a mapping from the handwriting domain to the signature domain. <s> BIB001 </s> Offline Handwritten Signature Verification - Literature Review <s> Datasets <s> The current need for large multimodal databases to evaluate automatic biometric recognition systems has motivated the development of the MCYT bimodal database. The main purpose has been to consider a large scale population, with statistical significance, in a real multimodal procedure, and including several sources of variability that can be found in real environments. The acquisition process, contents and availability of the single-session baseline corpus are fully described. Some experiments showing consistency of data through the different acquisition sites and assessing data quality are also presented. <s> BIB002 </s> Offline Handwritten Signature Verification - Literature Review <s> Datasets <s> This paper presents a set of geometric signature features for offline automatic signature verification based on the description of the signature envelope and the interior stroke distribution in polar and Cartesian coordinates. The features have been calculated using 16 bits fixed-point arithmetic and tested with different classifiers, such as hidden Markov models, support vector machines, and Euclidean distance classifier. The experiments have shown promising results in the task of discriminating random and simple forgeries. <s> BIB003 </s> Offline Handwritten Signature Verification - Literature Review <s> Datasets <s> 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. <s> BIB004
A large amount of research in automated signature verification has been conducted with private datasets. This makes it difficult to compare relate work, since an improvement in classification performance may be attributed to a better method, or simply to a cleaner or simpler database. In the last decade, however, a few signature datasets were made available publicly for the research community, addressing this gap. The process to acquire the signature images follows similar steps for most of the datasets. The genuine signatures are collected in one or more sessions, and require the user to provide several samples of their signatures. The user receives a form containing many cells, and provide a sample of his/her signature in each cell. The cells often have sizes to match common scenarios such as bank cheques and credit card vouchers BIB004 . The collection of forgeries follows a different process: the users receive samples from genuine signatures and are asked to imitate the signature one or more times. For some datasets, the same set of users that provided genuine signatures are used to provide forgeries (of other users' signatures), while in other datasets the forgeries were created using a different set of users. It is worth noting that the users that provide the forgeries are not experts in producing forgeries. Figure 3 illustrate the forms used to collect genuine signatures and forgeries for one of these datasets. After the forms are collected, they are scanned (often at 300 dpi or 600 dpi), and pre-processed. The preprocessing consists in separating the signatures into individual images, and for some datasets other actions are taken as well, such as binarization or noise removal. Table 1 presents a summary of the most commonly used signature datasets. The CEDAR dataset BIB001 was created with data from 55 users, that wrote their signatures in predefined spaces of 2 x 2 inches. At random, users were asked to create forgeries for signatures from other writers. Data was collected during three sessions to provide more variability for the signatures. For the MCYT datasets BIB002 ,  , the signatures were captured in an online (dynamic) and offline (static) formats. Users provided their signatures using a pen and paper templates over a pen tablet, and both the online information (e.g. position over time) as the offline information (signature image) were captured. For the GPDS datasets BIB004 , BIB003 , the genuine signatures were taken in a single session, using the form presented in figure 3 . A total of 1040 users provided their signatures. The analysis of the forms showed several cases where the users' signatures crossed the borders of the forms, resulting in the forms from 80 people being discarded (leaving data from 960 users). A different set of users (1920 people) participated in the creation of the forgeries. Forgers were allowed as much time as they wanted to produce the them. It is worth noting that some of the signatures of this dataset are no longer available as they were lost during a move -leaving signatures from 881 users available to use (details in 1 ).