Biometric Recognition Using Deep Learning: A Survey <s> Signature Recognition <s> Learning strategies and classification methods for verification of signatures from scanned documents are proposed and evaluated. Learning strategies considered are writer independent- those that learn from a set of signature sample (including forgeries) prior to enrollment of a writer, and writer dependent- those that learn only from a newly enrolled individual. Classification methods considered include two distance based methods (one based on a threshold, which is the standard method of signature verification and biometrics, and the other based on a distance probability distribution), a Nave Bayes (NB) classifier based on pairs of feature bit values and a support vector machine (SVM). Two scenarios are considered for the writer dependent scenario: (i) without forgeries (one-class problem) and (ii) with forgery samples being available (two class problem). The features used to characterize a signature capture local geometry, stroke and topology information in the form of a binary vector. In the one-class scenario distance methods are superior while in the two-class SVM based method outperforms the other methods. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Signature Recognition <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> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Signature Recognition <s> A new deep multitask learning based metric learning method is proposed.An appropriate architecture is proposed for offline signature verification.The knowledge of other signers' samples is transfered for better accuracy.The proposed method outperforms SVM and Discriminative Deep Metric Learning.It outperforms the accuracy of other published reports. Display Omitted This paper presents a novel classification method, Deep Multitask Metric Learning (DMML), for offline signature verification. Unlike existing methods that to verify questioned signatures of an individual merely consider the training samples of that class, DMML uses the knowledge from the similarities and dissimilarities between the genuine and forged samples of other classes too. To this end, using the idea of multitask and transfer learning, DMML train a distance metric for each class together with other classes simultaneously. DMML has a structure with a shared layer acting as a writer-independent approach, that is followed by separated layers which learn writer-dependent factors. We compare the proposed method against SVM, writer-dependent and writer-independent Discriminative Deep Metric Learning method on four offline signature datasets (UTSig, MCYT-75, GPDSsynthetic, and GPDS960GraySignatures) using Histogram of Oriented Gradients (HOG) and Discrete Radon Transform (DRT) features. Results of our experiments show that DMML achieves better performance compared to other methods in verifying genuine signatures, skilled and random forgeries. <s> BIB003
Signature is considered a behavioral biometric. It is widely used in traditional and digital formats to verify the user's identity for the purposes of security, transactions, agreements, etc. Therefore, being able to distinguish an authentic signature from a forged one is of utmost importance. Signature forgery can be performed as either a random forgery, where no attempt is made to make an authentic signature (e.g., merely writing the name  ), or a skilled forgery, where the signature is made to look like the original and is performed with the genuine signature in mind BIB003 . In order to distinguish an authentic signature from a forged one, one may either store merely signature samples to compare against (offline verification), or also the features of the written signature such as the thickness of a stroke and the speed of the pen during the signing BIB002 . For verification, there are writer-dependent (WD) and writer-independent (WI) methods. In WD methods, a classifier is trained for each signature owner, whereas, in WI methods, one is trained for all owners BIB001 .