Offline Handwritten Signature Verification - Literature Review <s> Shadow-code <s> The first stage of a complete automatic handwritten signature verification system (AHSVS) is described in this paper. Since only random forgeries are taken into account in this first stage of decision, the directional probability density function (PDF) which is related to the overall shape of the handwritten signature has been taken into account as feature vector. Experimental results show that using both directional PDFs and the completely connected feedforward neural network classifier are valuable to build the first stage of a complete AHSVS. > <s> BIB001 </s> Offline Handwritten Signature Verification - Literature Review <s> Shadow-code <s> In this paper, the authors present an evaluation of the extended shadow code (ESC) used as a global feature vector for the signature verification problem. The proposed class of shape factors seems to be a good compromise between global features related to the general aspect of the signature, and local features related to measurements taken on specific parts of the signature. This is achieved by the bar mask definition, where at low resolution the ESC is related to the overall proportions of the signature. At high resolution, values of the horizontal, vertical and diagonal bars could be related to local measurements taken on specific parts of the signature without requiring low-level handwriting segmentation which is a very difficult task. <s> BIB002 </s> Offline Handwritten Signature Verification - Literature Review <s> Shadow-code <s> Some of the fundamental problems facing handwritten signature verification are the large number of users, the large number of features, the limited number of reference signatures for training, the high intra-personal variability of the signatures and the unavailability of forgeries as counterexamples. This research first presents a survey of offline signature verification techniques, focusing on the feature extraction and verification strategies. The goal is to present the most important advances, as well as the current challenges in this field. Of particular interest are the techniques that allow for designing a signature verification system based on a limited amount of data. Next is presented a novel offline signature verification system based on multiple feature extraction techniques, dichotomy transformation and boosting feature selection. Using multiple feature extraction techniques increases the diversity of information extracted from the signature, thereby producing features that mitigate intra-personal variability, while dichotomy transformation ensures writer-independent classification, thus relieving the verification system from the burden of a large number of users. Finally, using boosting feature selection allows for a low cost writer-independent verification system that selects features while learning. As such, the proposed system provides a practical framework to explore and learn from problems with numerous potential features. Comparison of simulation results from systems found in literature confirms the viability of the proposed system, even when only a single reference signature is available. The proposed system provides an efficient solution to a wide range problems (eg. biometric authentication) with limited training samples, new training samples emerging during operations, numerous classes, and few or no counterexamples. <s> BIB003 </s> Offline Handwritten Signature Verification - Literature Review <s> Shadow-code <s> 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. <s> BIB004
Sabourin et al. BIB001 , BIB002 proposed an Extended Shadow Code for signature verification. A grid is overlaid on top of the signature image, containing horizontal, vertical and diagonal bars, each bar containing a fixed number of bins. Each pixel of the signature image is then projected to its closest bar in each direction (i.e. its "shadow" in the vertical / horizontal / diagonal bars), activating the respective bin. An example of this operator is presented in Figure 9 . After all pixels are projected to the respective bars, the number of active bins on each bar is counted, and used as a description of the signature. This feature extractor has been used by Rivard BIB003 and Eskander BIB004 with multiple resolutions, together with directional features, to achieve promising results on writer-independent and writer-dependent classification, respectively.