Offline Handwritten Signature Verification - Literature Review <s> Problem Statement <s> Some of the fundamental problems facing handwritten signature verification are the large number of users, the large number of features, the limited number of reference signatures for training, the high intra-personal variability of the signatures and the unavailability of forgeries as counterexamples. This research first presents a survey of offline signature verification techniques, focusing on the feature extraction and verification strategies. The goal is to present the most important advances, as well as the current challenges in this field. Of particular interest are the techniques that allow for designing a signature verification system based on a limited amount of data. Next is presented a novel offline signature verification system based on multiple feature extraction techniques, dichotomy transformation and boosting feature selection. Using multiple feature extraction techniques increases the diversity of information extracted from the signature, thereby producing features that mitigate intra-personal variability, while dichotomy transformation ensures writer-independent classification, thus relieving the verification system from the burden of a large number of users. Finally, using boosting feature selection allows for a low cost writer-independent verification system that selects features while learning. As such, the proposed system provides a practical framework to explore and learn from problems with numerous potential features. Comparison of simulation results from systems found in literature confirms the viability of the proposed system, even when only a single reference signature is available. The proposed system provides an efficient solution to a wide range problems (eg. biometric authentication) with limited training samples, new training samples emerging during operations, numerous classes, and few or no counterexamples. <s> BIB001 </s> Offline Handwritten Signature Verification - Literature Review <s> Problem Statement <s> 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. <s> BIB002 </s> Offline Handwritten Signature Verification - Literature Review <s> Problem Statement <s> Signature verification deals with the problem of identifying forged signatures of a user from his/her genuine signatures. The difficulty lies in identifying allowed variations in a user’s signatures, in the presence of high intra-class and low interclass variability (the forgeries may be more similar to a user’s genuine signature, compared to his/her other genuine signatures). The problem can be seen as a nonrigid object matching where classes are very similar. In the field of biometrics, signature is considered a behavioral biometric and the problem possesses further difficulties compared to other modalities (e.g. fingerprints) due to the added issue of skilled forgeries. A novel offline (image-based) signature verification system is proposed in this thesis. In order to capture the signature’s stable parts and alleviate the difficulty of global matching, local features (histogram of oriented gradients, local binary patterns) are used, based on gradient information and neighboring information inside local regions. Discriminative power of extracted features is analyzed using support vector machine (SVM) classifiers and their fusion gave better results compared to state-of-the-art. Scale invariant feature transform (SIFT) matching is also used as a complementary approach. Two different approaches for classifier training are investigated, namely global and user-dependent SVMs. User-dependent SVMs, trained separately for each user, learn to differentiate a user’s (genuine) reference signatures from other signatures. On the other hand, a single global SVM trained with difference vectors of query and reference signatures’ features of all users in the training set, learns how to weight the importance of different types of dissimilarities. The fusion of all classifiers achieves a 6.97% equal error rate in skilled forgery tests using the public GPDS-160 signature database. Former versions of the system have won several signature verification competitions such as first place in 4NSigComp2010 and 4NSigComp2012 (the task without disguised signatures); first place in 4NSigComp2011 for Chinese signatures category; first place in SigWiComp2013 for all categories. Obtained results are better than those reported in the literature. One of the major benefits of the proposed method is that user enrollment does not require skilled forgeries of the enrolling user, which is essential for real life applications. <s> BIB003
In the literature of Offline Signature Verification, we can find multiple ways of defining the problem. In particular, one matter is critical to be able to compare related work: whether or not skilled forgeries are used for training. Some authors do not use skilled forgeries at all for training (e.g. BIB001 , BIB002 ), other researchers use skilled forgeries for training writer-independent classifiers, testing these classifiers in a separate set of users (e.g. BIB003 ); lastly, some papers use skilled forgeries for training writer-dependent classifiers, and test these classifiers in a separate set of genuine signatures and forgeries from the same set of users. In this report, we are concerned with the design of an Offline Signature Verification System that could be used in practice. For this reason, we restrict our analysis to methods that do not rely on skilled forgeries for the users enrolled in the system, since this is not the case in practical applications. We do consider, however, that a dataset consisting of genuine signatures and forgeries is available for training writer-independent classifiers, where the users from this dataset are not used for evaluating the performance of the classifier. We first consider a development set D containing samples from a set of users Y D . The development dataset is composed of genuine and skilled forgeries for the users in Y D : D = {D genuine D skilledF org }. Each of these sets contain M pairs of signature samples, and their label: {(X, Y ) (m) }, n = 1...M . For genuine signatures, the label represents from which user the signature comes from, and for forgeries it represents for which user the forgery was created. Next, we consider a learning set L, containing samples from a set of users Y. These are the users enrolled to the system, and the learning set consists of the samples obtained in the enrolment process. Therefore, this set only contains the genuine signatures for each user: L = {L genuine }. For evaluating the performance of the system, we consider a testing set T , containing samples from the same set of users Y. This set represents the new signatures (genuine and forgeries) presented to the system after it has been trained. This dataset consists of genuine signatures and skilled forgeries: This problem is then addressed as a Pattern Recognition problem, where the datasets D and L are used to train a classifier (estimate the parameters of a model), and then are used to generalize to unseen examples. The quality of the model is evaluated using the testing set T . Commonly, the following steps are taken: (image) preprocessing, feature extraction, followed by training a classifier. These steps are reviewed in detail in the subsequent sections of this report. 