Offline Handwritten Signature Verification - Literature Review <s> Geometric Features <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> BIB001 </s> Offline Handwritten Signature Verification - Literature Review <s> Geometric Features <s> We propose an HMM-based approach for off-line signature verification. One of the novelty aspects of our method lies in the ability to dynamically and automatically derive the various author-dependent parameters, required to set an optimal decision rule for the verification process. In this context, the cross-validation principle is used to derive not only the best HMM models, but also an optimal acceptation/rejection decision threshold for each author. This leads to a high discrimination between actual authors and impostors in the context of random forgeries. To quantitatively evaluate the generalization capabilities of our approach, we considered two conceptually different experimental tests carried out on two sets of 40 and 60 authors respectively, each author providing 40 signatures. The results obtained on these two sets show the robustness of our approach. <s> BIB002 </s> Offline Handwritten Signature Verification - Literature Review <s> Geometric Features <s> The objective of this work is to present an off-line signature verification system. It is basically divided into three parts. The first one describes a pre-processing process, a segmentation process and a feature extraction process, in which the main aim is to obtain the maximum performance quality of the process of verification of random falsifications, in the false acceptance and false rejection concept. The second presents a learning process based on HMM, where the aim is obtaining the best model, that is, one that is capable of representing each writer's signature, absorbing yet at the same time discriminating, at most the intra-personal variation and the interpersonal variation. A third and last part, presents a signature verification process that uses the models generated by the learning process without using any prior knowledge of test data, in other words, using an automatic derivation process of the decision thresholds. <s> BIB003 </s> Offline Handwritten Signature Verification - Literature Review <s> Geometric Features <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
Geometric features measure the overall shape of a signature. This category includes basic descriptors, such as the signature height, width, caliber (height-to-width ration) and area. More complex descriptors include the count of endpoints and closed loops BIB004 Besides using global descriptors, several authors also generate local geometric features by dividing the signature in a Cartesian grid and calculating features from each cell of the grid. For example, using the pixel density within grids ( BIB004 , BIB002 , BIB003 ). In particular, Huang and Yan BIB001 used several types of signature representations (outline, skeleton, etc.) as input, divided each representation in a grid format, and used the pixel density in each cell of the grids as the features for the signature.