Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Signature Recognition <s> The computational power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections can allow a significant fraction of the knowledge of the system to be applied to an instance of a problem in a very short time. One kind of computation for which massively parallel networks appear to be well suited is large constraint satisfaction searches, but to use the connections efficiently two conditions must be met: First, a search technique that is suitable for parallel networks must be found. Second, there must be some way of choosing internal representations which allow the preexisting hardware connections to be used efficiently for encoding the constraints in the domain being searched. We describe a general parallel search method, based on statistical mechanics, and we show how it leads to a general learning rule for modifying the connection strengths so as to incorporate knowledge about a task domain in an efficient way. We describe some simple examples in which the learning algorithm creates internal representations that are demonstrably the most efficient way of using the preexisting connectivity structure. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Signature Recognition <s> Reliable identification and verification of off-line handwritten signatures from images is a difficult problem with many practical applications. This task is a difficult vision problem within the field of biometrics because a signature may change depending on psychological factors of the individual. Motivated by advances in brain science which describe how objects are represented in the visual cortex, advanced research on deep neural networks has been shown to work reliably on large image data sets. In this paper, we present a deep learning model for off-line handwritten signature recognition which is able to extract high-level representations. We also propose a two-step hybrid model for signature identification and verification improving the misclassification rate in the well-known GPDS database. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Signature Recognition <s> The handwritten signature is widely employed and accepted as a proof of a person’s identity. In our everyday life, it is often verified manually, yet only casually. As a result, the need for automatic signature verification arises. In this paper, we propose a new approach to the writer independent verification of offline signatures. Our approach, named Signature Embedding, is based on deep metric learning. Comparing triplets of two genuine and one forged signature, our system learns to embed signatures into a high-dimensional space, in which the Euclidean distance functions as a metric of their similarity. Our system ranks best in nearly all evaluation metrics from the ICDAR SigWiComp 2013 challenge. The evaluation shows a high generality of our system: being trained exclusively on Latin script signatures, it outperforms the other systems even for signatures in Japanese script. <s> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on 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> BIB004 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Signature Recognition <s> We present an offline signature verification system with new architecture, using Deep Convolutional Generative Adversarial Networks (DCGANs) to learn features unsupervised instead of using hand-crafted features. The advantage of this architecture is that system has a robust generalization ability. Besides, aiming at the conflict between convenience and accuracy, a hybrid Writer-Independent-Writer-Dependent classifier is used, which is an approach compromise two different kind of classifier. We test our method in GPDSsyntheticSignature database, which is an updated version of GPDS-960. In order to make it's convenient to compare with other works, we also use GPDS-960 to test our method. Our experimental results show that, the accuracy of proposed method is satisfactory. Due to this kind of architecture, the more query samples are tested and enrolled into the system, the more accurate our system will be conceptually. <s> BIB005 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Signature Recognition <s> We propose formulations for learning features for Offline Signature Verification.A novel method that uses knowledge of forgeries from a subset of users is proposed.Learned features are used to train classifiers for other users (without forgeries).Experiments on GPDS-960 show a large improvement in state-of-the-art.Results in other 3 datasets show that the features generalize without fine-tuning. Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a persons signature and deliberately attempt to imitate it. In offline (static) signature verification, the dynamic information of the signature writing process is lost, and it is difficult to design good feature extractors that can distinguish genuine signatures and skilled forgeries. This reflects in a relatively poor performance, with verification errors around 7% in the best systems in the literature. To address both the difficulty of obtaining good features, as well as improve system performance, we propose learning the representations from signature images, in a Writer-Independent format, using Convolutional Neural Networks. In particular, we propose a novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user. Extensive experiments were conducted on four datasets: GPDS, MCYT, CEDAR and Brazilian PUC-PR datasets. On GPDS-160, we obtained a large improvement in state-of-the-art performance, achieving 1.72% Equal Error Rate, compared to 6.97% in the literature. We also verified that the features generalize beyond the GPDS dataset, surpassing the state-of-the-art performance in the other datasets, without requiring the representation to be fine-tuned to each particular dataset. <s> BIB006 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Signature Recognition <s> Systems based on deep neural networks have made a breakthrough in many different pattern recognition tasks. However, the use of these systems with traditional architectures seems not to work properly when the amount of training data is scarce. This is the case of the on-line signature verification task. In this paper, we propose a novel writer-independent on-line signature verification systems based on Recurrent Neural Networks (RNNs) with a Siamese architecture whose goal is to learn a dissimilarity metric from the pairs of signatures. To the best of our knowledge, this is the first time these recurrent Siamese networks are applied to the field of on-line signature verification, which provides our main motivation. We propose both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) systems with a Siamese architecture. In addition, a bidirectional scheme (which is able to access both past and future context) is considered for both LSTM- and GRU-based systems. An exhaustive analysis of the system performance and also the time consumed during the training process for each recurrent Siamese network is carried out in order to compare the advantages and disadvantages for practical applications. For the experimental work, we use the BiosecurID database comprised of 400 users who contributed a total of 11,200 signatures in four separated acquisition sessions. Results achieved using our proposed recurrent Siamese networks have outperformed the state-of-the-art on-line signature verification systems using the same database. <s> BIB007
Before the rise of deep learning to its current popularity, there were a few works seeking to use it. For example, Ribeiro et al BIB002 proposed a deep learningbased method to both identify a signature's owner and distinguish an authentic signature from a fake, making use of the Restricted Boltzmann Machine (RBM) BIB001 . With more powerful computer and massively parallel architectures making deep learning mainstream, the number of deep learning-based works increased dramatically, including those involving signature recognition. Rantzsch et al BIB003 proposed an embedding-based WI offline signature verification, in which the input signatures are embedded in a high-dimensional space using a specific training pattern, and the Euclidean distance between the input and the embedded signatures will determine the outcome. Soleimani et al BIB004 proposed Deep Multitask Metric Learning (DMML), a deep neural network used for offline signature verification, mixing WD methods, WI methods, and transfer learning. Zhang et al BIB005 proposed a hybrid WD-WI classifier in conjuction with a DC-GAN network in order to learn to extract the signature features in an unsupervised manner. With signature being a behavioral biometric, it is imperative to learn the best features to distinguish an authentic signature from a forged one. Hafemann et al BIB006 proposed a WI CNN-based system to learn features of forgeries from multiple datasets, which greatly reduced the error equal rate compared to that of the state-of-the-art. Wang et al  proposed signature identification using a special GAN network (SIGAN) in which the loss value from the discriminator network is utilized as the threshold for the identification process. Tolosana et al BIB007 proposed an online writer-independent signature verification method using Siamese recurrent neural networks (RNNs), including long short term memory (LSTM) and gated recurrent units (GRUs).