You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Offline Handwritten Signature Verification - Literature Review 
Chapter Title:  Support Vector Machines 
1. Abstract of Cited Paper (BIB001):  In this paper we present an off-line signature verification and recognition system using the global, directional and grid fea-tures of signatures. Support Vector Machine (SVM) was used to verify and classify the signatures and a classification ratio of 0.95 was obtained. As the recognition of signatures repre-sents a multiclass problem SVM's one-against-all method was used. We also compare our methods performance with Artifical Neural Network's (ANN) backpropagation method. 
2. Abstract of Cited Paper (BIB002):  The SVM is a new classification technique in the field of statistical learning theory which has been applied with success in pattern recognition applications like face and speaker recognition, while the HMM has been found to be a powerful statistical technique which is applied to handwriting recognition and signature verification. This paper reports on a comparison of the two classifiers in off-line signature verification. For this purpose, an appropriate learning and testing protocol was created to observe the capability of the classifiers to absorb intrapersonal variability and highlight interpersonal similarity using random, simple and simulated forgeries. 
3. Abstract of Cited Paper (BIB003):  In this work we address two important issues of off-line signature verification. The first one regards feature extraction. We introduce a new graphometric feature set that considers the curvature of the most important segments, perceptually speaking, of the signature. The idea is to simulate the shape of the signature by using Bezier curves and then extract features from these curves. The second important aspect is the use of an ensemble of classifiers based on graphometric features to improve the reliability of the classification, hence reducing the false acceptance. The ensemble was built using a standard genetic algorithm and different fitness functions were assessed to drive the search. Two different scenarios were considered in our experiments. In the former, we assume that only genuine signatures and random forgeries are available to guide the search. In the latter, on the other hand, we assume that simple and simulated forgeries also are available during the optimization of the ensemble. The pool of base classifiers is trained using only genuine signatures and random forgeries. Thorough experiments were conduct on a database composed of 100 writers and the results compare favorably. 
4. Abstract of Cited Paper (BIB004):  The paper presents a novel set of features based on surroundedness property of a signature (image in binary form) for off-line signature verification. The proposed feature set describes the shape of a signature in terms of spatial distribution of black pixels around a candidate pixel (on the signature). It also provides a measure of texture through the correlation among signature pixels in the neighborhood of that candidate pixel. So the proposed feature set is unique in the sense that it contains both shape and texture property unlike most of the earlier proposed features for off-line signature verification. Since the features are proposed based on intuitive idea of the problem, evaluation of features by various feature selection techniques has also been sought to get a compact set of features. To examine the efficacy of the proposed features, two popular classifiers namely, multilayer perceptron and support vector machine are implemented and tested on two publicly available database namely, GPDS300 corpus and CEDAR signature database. 
5. Abstract of Cited Paper (BIB005):  The limited number of writers and genuine signatures constitutes the main problem for designing a robust Handwritten Signature Verification System (HSVS). We propose, in this paper, the use of One-Class Support Vector Machine (OC-SVM) based on writer-independent parameters, which takes into consideration only genuine signatures and when forgery signatures are lack as counterexamples for designing the HSVS. The OC-SVM is effective when large samples are available for providing an accurate classification. However, available handwritten signature samples are often reduced and therefore the OC-SVM generates an inaccurate training and the classification is not well performed. In order to reduce the misclassification, we propose a modification of decision function used in the OC-SVM by adjusting carefully the optimal threshold through combining different distances used into the OC-SVM kernel. Experimental results conducted on CEDAR and GPDS handwritten signature datasets show the effective use of the proposed system comparatively to the state of the art. We propose handwritten signature verification for writer independent parameters.We propose to design HSVS system using only genuine signatures using OC-SVM.Applying a soft threshold in order to reduce the misclassification of the OC-SVM.Combination scheme is proposed through versus distances used into the OC-SVM kernel.Competitive results are obtained comparatively to the state of the art.

Literature Review:
In its original formulation, SVMs are used for two-class classification problems, and learn a hyperplane that maximizes the margin, that is, the distance between the hyperplane and the samples of each class closest to this hyperplane. A second formulation of the model considers a "soft margin", Figure 13 : The steps for training an HMM for signature verification  to enable the classification of problems that are not linearly separable BIB001 . The effectiveness of the model is further increased by the usage of the "kernel trick", that allows the classification in a higher-dimensional feature space implicitly, by the usage of a kernel function. Support Vector Machines have been extensively used for signature verification, for both writerdependent and writer-independent classification BIB002 , BIB003 , BIB004 , BIB005 . In recent years, Guerbai et al BIB006 used One-Class SVMs for the task. This type of model attempt to only model one class (in the case of signature verification, only the genuine signatures), which is a desirable property, since for the actual users enrolled in the system we only have the genuine signatures to train the model. However, even in this scenario there is a need to use forgeries (in their case, random forgeries) to define the thresholds of the classifiers.