Offline Handwritten Signature Verification - Literature Review <s> Texture features <s> This thesis presents extensions to the local binary pattern (LBP) texture analysis operator. The operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. It is made invariant against the rotation of the image domain, and supplemented with a rotation invariant measure of local contrast. The LBP is proposed as a unifying texture model that describes the formation of a texture with micro-textons and their statistical placement rules. The basic LBP is extended to facilitate the analysis of textures with multiple scales by combining neighborhoods with different sizes. The possible instability in sparse sampling is addressed with Gaussian low-pass filtering, which seems to be somewhat helpful. Cellular automata are used as texture features, presumably for the first time ever. With a straightforward inversion algorithm, arbitrarily large binary neighborhoods are encoded with an eight-bit cellular automaton rule, resulting in a very compact multi-scale texture descriptor. The performance of the new operator is shown in an experiment involving textures with multiple spatial scales. An opponent-color version of the LBP is introduced and applied to color textures. Good results are obtained in static illumination conditions. An empirical study with different color and texture measures however shows that color and texture should be treated separately. A number of different applications of the LBP operator are presented, emphasizing real-time issues. A very fast software implementation of the operator is introduced, and different ways of speeding up classification are evaluated. The operator is successfully applied to industrial visual inspection applications and to image retrieval. <s> BIB001 </s> Offline Handwritten Signature Verification - Literature Review <s> Texture features <s> In this paper, we propose new data features to improve the off-line handwritten signature verification. The proposed features combine advantages of LBP and topological characteristics. Specifically, the Orthogonal Combination of LBP, which provides an LBP histogram with a reduced size, is combined with a topological descriptor that is called longest run features. The verification task is achieved by SVM classifiers and the performance assessment is conducted comparatively to the basic LBP descriptors. Results obtained on both GPDS 300 and CEDAR datasets show that the proposed features improve the verification accuracy while reducing the data size. <s> BIB002 </s> Offline Handwritten Signature Verification - Literature Review <s> Texture features <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
Texture features, in particular variants of Local Binary Patterns (LBP), have been used in many experiments in recent years. The LBP operator was introduced by Ojala  as a discriminant feature extractor for texture images. The original LBP operator is illustrated in Figure 10 -the objective is to determine the pattern of the local neighborhood of each pixel. Each of the 8 neighboring positions is associated a code that goes from 2 0 to 2 7 . The LBP code for the neighborhood of a pixel is then found by adding the codes of all neighbors that have a pixel value larger than the central pixel. Commonly, the LBP codes for all pixels in the image are calculated, and histograms of these codes are Figure 10 : Example application of the original LBP operator  . In this example, the LBP code for the central pixel is 1 + 8 + 32 + 128 = 169 used as features. Several extensions of LBP have been proposed, most commonly a derivation that considers circular neighbors, equally spaced BIB001 . In this case, the LBP code has two parameters: P and R, where P is the number of neighbors, and R is the radius (distance from the central point). On top of this derived version of LBP, several extensions have been proposed, for instance to group multiple LBP codes together, as to achieve rotation invariance. In the context of signature verification, LBP has been used by Yilmaz ([35] , BIB003 ), together with other descriptors to achieve state-of-the-art results on the GPDS dataset, considering models that do not use skilled forgeries for training. Serdouk et. al BIB002 ,  used an Orthogonal Combinational LBP and Rotation Invariant LBP features to obtain state-of-the-art results in the GPDS dataset, using skilled forgeries for training the models.