Offline Handwritten Signature Verification - Literature Review <s> Preprocessing <s> In this paper a multi-expert signature verification system is presented. The system has been specifically designed for applications in the field of bankcheck processing. For this purpose, it combines three different algorithms for signature verification. A wholistic approach is used in the first algorithm, a component-oriented approach is used in the second and third algorithms. The second algorithm is based on a structure-based procedure, the third algorithm uses a highly-adaptive neural network. The three algorithms are combined in the multi-expert system by a voting strategy. <s> BIB001 </s> Offline Handwritten Signature Verification - Literature Review <s> Preprocessing <s> In this paper a method for off-line signature verification based on geometric feature extraction and neural network classification is proposed. The role of signature shape description and shape similarity measure is discussed in the context of signature recognition and verification. Geometric features of input signature image are simultaneously examined under several scales by a neural network classifier. An overall match rating is generated by combining the outputs at each scale. Artificially generated genuine and forgery samples from enrollment reference signatures are used to train the network, which allows definite training control and at the same time significantly reduces the number of enrollment samples required to achieve a good performance. Experiments show that 90% correct classification rate can be achieved on a database of over 3000 signature images. <s> BIB002 </s> Offline Handwritten Signature Verification - Literature Review <s> Preprocessing <s> Extracting a signature from a check with a patterned background is a thorny problem in image segmentation. Methods based on threshold techniques often necessitate meticulous postprocessing in order to correctly capture the handwritten information. In this study, we tackle the problem of extracting handwritten information by means of an intuitive approach that is close to human visual perception, defining a topological criterion specific to handwritten lines which we call filiformity. This approach was inspired by the existence in the human eye of cells whose specialized task is the extraction of lines. First, we define two topological measures of filiformity for binary objects. Next, we extend these measures to include gray-level images. One of these measures, which is particularly interesting, differentiates the contour lines of objects from the handwritten lines we are trying to isolate. The local value provided by this measure is then processed by global thresholding, taking into account information about the whole image. This processing step ends with a simple fast algorithm. Evaluation of the extraction algorithm carried out on 540 checks with 16 different background patterns demonstrates the robustness of the algorithm, particularly when the background depicts a scene. <s> BIB003 </s> Offline Handwritten Signature Verification - Literature Review <s> Preprocessing <s> Abstract This paper presents a new technique for off-line signature recognition and verification. The proposed system is based on global, grid and texture features. For each one of these feature sets a special two stage Perceptron OCON (one-class-one-network) classification structure has been implemented. In the first stage, the classifier combines the decision results of the neural networks and the Euclidean distance obtained using the three feature sets. The results of the first-stage classifier feed a second-stage radial base function (RBF) neural network structure, which makes the final decision. The entire system was extensively tested and yielded high recognition and verification rates. <s> BIB004 </s> Offline Handwritten Signature Verification - Literature Review <s> Preprocessing <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> BIB005 </s> Offline Handwritten Signature Verification - Literature Review <s> Preprocessing <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> BIB006 </s> Offline Handwritten Signature Verification - Literature Review <s> Preprocessing <s> In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field. <s> BIB007 </s> Offline Handwritten Signature Verification - Literature Review <s> Preprocessing <s> Signature verification and Identification has great importance in authentication systems. Persian signatures are different from other signature types because people usually do not use text in it and they draw a shape as their signature. In this paper, a method for off-line Persian signature identification and verification is proposed that is based on DWT (Discrete Wavelet Transform) and Image Fusion. To extract features, at first DWT is used to access details of signature; then several signatures instances of each person are fused together to generate reference pattern of person's signature. In the classification phase, Euclidean distance between the test image and each pattern is used in each sub band. Experimental results confirmed the effectiveness of proposed method. <s> BIB008 </s> Offline Handwritten Signature Verification - Literature Review <s> Preprocessing <s> In this paper, a new offline handwritten signature identification and verification system based on Contourlet transform is proposed. Contourlet transform (CT) is used as feature extractor in proposed system. Signature image is enhanced by removing noise and then it is normalized by size. After preprocessing stage, by applying a special type of Contourlet transform on signature image, related Contourlet coefficients are computed and feature vector is created. Euclidean distance is used as classifier. One of the most important features of proposed system is its independency from signer’s nationality. Experimental results show that proposed system has so reliable results for both Persian and English signatures. <s> BIB009 </s> Offline Handwritten Signature Verification - Literature Review <s> Preprocessing <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> BIB010
As with most pattern recognition problems, preprocessing plays an important role in signature verification. Signature images may present variations in terms of pen thickness, scale, rotation, etc., even among authentic signatures of a person. Common preprocessing techniques are: signature extraction, noise removal, application of morphological operators, size normalization, centering and binarization BIB007 . • Signature extraction -This is an initial step that consists in finding and extracting a signature from a document. This is a particular challenging problem in bank cheques, where the signature is often written on top of a complex background BIB001 , BIB003 . This step is, however, not considered in most signature verification studies, that already consider signatures extracted from the documents, that is, each input image containing a single signature, commonly with no background and little noise. • Noise Removal -The digital version of the signature images is commonly obtained by the use of scanners, and this process may produce noise in the signatures, such as single black pixels on a white background or single white pixels on a black background. A common strategy is to apply a noise removal filter to the image, such a median filter BIB002 . It is also common to apply morphological operations to fill small holes and remove small regions of connected components (e.g. less than 20 black pixels on the component, in a binarized image) BIB002 [8]. • Size normalization and centering -Depending on the properties of the features to be used, a few strategies for size normalization have been adopted. The simplest strategy is just to crop the signature images to have a "tight fit" on the signature, that is, crop the image, such that the frame touches the signature in the four directions (left, right, top, bottom) BIB008 . Another strategy is to user an even narrower bounding box, such as cutting strokes that are far from the image centroid, that are often subject to more variance in a user's signature BIB010 . Other authors set a fixed desired width, keeping the height-to-width ratio unchanged BIB004 , or setting a fixed frame size (width and height), and centering the signature in this frame BIB009 . When setting a fixed width and height, the signature is usually centered: the centroid of the signature is calculated and used to center the image, adding white borders to fill the remainder of the image BIB002 . • Signature representation -Besides just using the gray-level image as input, other representations for the signature have been considered. Huang et al. BIB002 consider the signature's skeleton, outline, ink distribution, high pressure regions and directional frontiers. An example of these different representations is found in Figure 4 . Ferrer et al BIB006 used a morphological operator to dilate the signature image and fill it, to obtain an outline ( Figure  5 ) • Signature Alignment -alignment is a common strategy in online signature verification, but not broadly applied for the offline scenario. Yilmaz BIB010 propose aligning the signatures for training, by applying rotation, scaling and translation. The objective is to maximize the similarity of each pair of signatures, by minimizing the distance between their feature vectors, after applying the transformations. The best parameters (rotation angle, scaling factor and number of pixels in the translation) are found by an exhaustive search. Yilmaz reported increased accuracy by aligning the signatures during training, but found that aligning the queries during testing did not improve accuracy, while increasing the computational cost. Kalera et al. BIB005 propose a method to perform Rotation normalization, using first and second order moments of the signature image. Figure 6 : Examples of static graphometric features  5 Feature Extraction After the signatures have been acquired and pre-processed, the next step is to extract discriminant features from the signature images. In the last decades, offline signature verification has been studied from many perspectives, yielding multiple alternatives for feature extraction. Broadly speaking, the feature extraction techniques can be classified as Static or Pseudo-dynamic. Using static features is the most straightforward approach, given the nature of the offline signature verification problem. Pseudo-dynamic features attempt to recover dynamic information from the signature execution process (such as speed, pressure, etc.). Another broad categorization of the feature extraction methods is between Global and Local features. Global features describe the signature images as a whole -for example, features such as height, width of the signature, or in general feature extractors that are applied to the entire signature image. In contrast, local features describe parts of the images, either by segmenting the image (e.g. according to connected components) or most commonly by the dividing the image in a grid (of Cartesian or polar coordinates), and applying feature extractors in each part of the image. The following sections describe the most common feature descriptors used in the literature.