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:  Writer-independent Classification 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  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.

Literature Review:
Some authors have adopted a writer-independent approach for the signature verification problem BIB001 , BIB004 , BIB002 , BIB003 . The objective is to train a classifier on a set of the users, and then use this classifier in a disjoint set of users. In particular, the usage of the dissimilarity representation (or dichotomy transformation) has shown to be promising for this problem. The idea of the dissimilarity representation is to transform an N-class problem into a 2-class problem. This transformation is applied to pairs of samples in the feature space, obtaining new samples from a positive class (when the two original samples belong to the same class) and negative class (when the two original samples belong to different classes). A common approach for this transformation is to simply use the absolute value of the difference between two feature vectors: In this case, the resulting feature vector (in the dissimilarity space) has the same cardinality (number of features) as the original samples. This procedure is illustrated in figure 14 . One important advantage of this model is that even with a small number of samples in the feature representation, it is possible to generate a large number of samples in the dissimilarity space. This approach also has the benefit of only requiring one model to be trained (instead of one model per user), which is an interesting property for deploying this model in a real application.