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.
Methods to learn features from data have not yet been widely explored for offline signature verification. @cite_16 used Restricted Boltzmann Machines (RBMs) to learn features from signature images. However, in this work they only showed the visual appearance of the weights, and did not test the features for classification. Khalajzadeh @cite_29 used Convolutional Neural Networks (CNNs) for signature verification on a dataset of Persian signatures, but only considered the classification between different users (e.g. detecting random forgeries), and did not considered skilled forgeries. @cite_15 proposed a solution using deep neural networks for Multitask Metric Learning. In their work, a distance metric between pairs of signatures is learned. Contrary to our work, the authors used handcrafted feature extractors (LBP in the experiments with the GPDS dataset), while in our work the inputs to the system are the signature themselves (pixel intensities), and the feature representation is learned. In a similar vein to our work, Eskander @cite_19 presented a hybrid Writer-Independent Writer-Dependent solution, using a Development dataset for feature selection, followed by training WD classifiers using the selected features. However, in the present work we use a Development dataset for feature learning instead of feature selection.
@cite_19 @cite_29 @cite_16 @cite_15
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We present an offline signature verification system using three different pseudo-dynamic features, two different classifier training approaches and two datasets. One of the most difficult problems of off-line signature verification is that the signature is just a static image while losing a lot of useful dynamic information. Three separate pseudo-dynamic features based on gray level: local binary pattern (LBP), gray level co-occurrence matrix (GLCM) and histogram of oriented gradients (HOG) are used. The classification is performed using writer-dependent Support Vector Machine (SVMs) classifiers and Global Real Adaboost method, where two different approaches to train the classifier. In the first mode, each SVM is trained with the feature vectors obtained from the reference signatures of the corresponding user and those random forgeries for each signer while the global Adaboost classifier is trained using genuine and random forgery signatures of signers that are excluded from the test set. The fusion of all features achieves the best result of 7.66 and 9.94 equal error rate in GPDS while 7.55 and 11.55 equal error rate in CSD respectively. 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. Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic feature learning, i.e., learning without requiring manual annotation effort, is of crucial importance in order to successfully harvest the vast amount of visual data that are available today. In our work we propose to learn image features by training ConvNets to recognize the 2d rotation that is applied to the image that it gets as input. We demonstrate both qualitatively and quantitatively that this apparently simple task actually provides a very powerful supervisory signal for semantic feature learning. We exhaustively evaluate our method in various unsupervised feature learning benchmarks and we exhibit in all of them state-of-the-art performance. Specifically, our results on those benchmarks demonstrate dramatic improvements w.r.t. prior state-of-the-art approaches in unsupervised representation learning and thus significantly close the gap with supervised feature learning. For instance, in PASCAL VOC 2007 detection task our unsupervised pre-trained AlexNet model achieves the state-of-the-art (among unsupervised methods) mAP of 54.4 that is only 2.4 points lower from the supervised case. We get similarly striking results when we transfer our unsupervised learned features on various other tasks, such as ImageNet classification, PASCAL classification, PASCAL segmentation, and CIFAR-10 classification. The code and models of our paper will be published on: this https URL . A state-of-art offline signature verification system is presented.Complementary approaches and features are fused at multiple levels.Useful local binary patterns are selected for offline signature verification.In-depth analysis of the effects of alignment for registering signatures is done.A new feature is proposed from SIFT keypoint alignment. Offline signature verification is a task that benefits from matching both the global shape and local details; as such, it is particularly suitable to a fusion approach. We present a system that uses a score-level fusion of complementary classifiers that use different local features (histogram of oriented gradients, local binary patterns and scale invariant feature transform descriptors), where each classifier uses a feature-level fusion to represent local features at coarse-to-fine levels. For classifiers, two different approaches are investigated, namely global and user-dependent classifiers. User-dependent classifiers are trained separately for each user, to learn to differentiate that users genuine signatures from other signatures; while a single global classifier is trained with difference vectors of query and reference signatures of all users in the training set, to learn the importance of different types of dissimilarities.The fusion of all classifiers achieves a state-of-the-art performance with 6.97 equal error rate in skilled forgery tests using the public GPDS-160 signature database. The proposed system does not require skilled forgeries of the enrolling user, which is essential for real life applications.