Offline Handwritten Signature Verification - Literature Review <s> Model Training <s> In practice, each writer provides only a limited number of signature samples to design a signature verification (SV) system. Hybrid generative-discriminative ensembles of classifiers (EoCs) are proposed in this paper to design an off-line SV system from few samples, where the classifier selection process is performed dynamically. To design the generative stage, multiple discrete left-to-right Hidden Markov Models (HMMs) are trained using a different number of states and codebook sizes, allowing the system to learn signatures at different levels of perception. To design the discriminative stage, HMM likelihoods are measured for each training signature, and assembled into feature vectors that are used to train a diversified pool of two-class classifiers through a specialized Random Subspace Method. During verification, a new dynamic selection strategy based on the K-nearest-oracles (KNORA) algorithm and on Output Profiles selects the most accurate EoCs to classify a given input signature. This SV system is suitable for incremental learning of new signature samples. Experiments performed with real-world signature data (composed of genuine samples, and random, simple and skilled forgeries) indicate that the proposed dynamic selection strategy can significantly reduce the overall error rates, with respect to other EoCs formed using well-known dynamic and static selection strategies. Moreover, the performance of the SV system proposed in this paper is significantly greater than or comparable to that of related systems found in the literature. <s> BIB001 </s> Offline Handwritten Signature Verification - Literature Review <s> Model Training <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> Model Training <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
We now discuss the actual machine learning models used for the task of signature verification. The models can be broadly classified in two groups: writer-dependent and writer-independent. In the first case, that is more common in the literature, a model is trained for each user, using the user's genuine signatures, and commonly random forgeries (by using genuine signature from other users). During the testing phase, a new signature is input to the model, that classifies a sample as genuine or forgery. The writer-independent approach, on the other hand, involves only a single classifier for all users. In this case, usually a distinct set of users is used for training and testing, and during the test phase both the model is used, as well as reference genuine samples for each user, to make a decision (genuine or forgery). Some authors use a combination of both approaches. For example, Eskander et al BIB002 trained a hybrid writer-independent-writer-dependent solution, where a writer-independent classifier is used for classification until a reasonable number of genuine samples for the user is obtained -at this point a writer-dependent classifier is trained and used for subsequent queries. Yilmaz BIB003 propose a hybrid approach, where the results of both a writer-independent and writer-dependent classifiers are combined. As a third scenario, some authors train writer-dependent classifiers, but due to the low number of samples, optimize the models' hyperparameters in a writer-independent format (as in BIB001 ). Besides the most basic classifiers (e.g. simple thresholding and nearest-neighbors), several strategies have been tried for the task of signature verification. The following sections cover the main models used for the task.