In this paper, the specific trait of Persian SIGNATUREs is applied to SIGNATURE VERIFICATION. Efficient features, which can discriminate among Persian SIGNATUREs, are investigated in this approach. Persian SIGNATUREs, in comparison with other languages SIGNATUREs, have more curvature and end in a specific style. An experiment has been designed to determine the function indicating the most robust features of Persian SIGNATUREs. To improve the performance of VERIFICATION, a combination of shape based and dynamic extracted features is applied to Persian SIGNATURE VERIFICATION. To classify these SIGNATUREs, Support Vector Machine (SVM) is applied. The proposed method is examined on two common Persian datasets, the new proposed Persian dataset in this paper (Noshirvani Dynamic SIGNATURE Dataset) and an international dataset (SVC2004). For three Persian datasets EER value are equal to 3, 3.93, 4.79, while for SVC2004 the EER value is 4.43.These experiments led to identification of new features combinations that are more robust. The results show the over performance of these features among all of the previous works on the Persian SIGNATURE databases; however, it does not reach the best reported results in an international database. This can be deduced that language specific approaches may show better results.

Many methods are introduced for estimating the similarities or differences of time signals. One of theses methods, DTW algorithm, is also a utility for other domains including classification, data mining and matching regions between two time signals. DTW algorithm minimizes points distance between two signals by contracting or expanding the time axes to find the corresponding points. In this paper, with modification of the local constraints in DTW, a powerful method is proposed for measuring the global or local similarities between two signals. In addition to increasing the accuracy of signals distance measurements and decreasing the classification error, proposed algorithm is more stable than classic DTW against variations of structure and time signal source. The proposed method for dynamic SIGNATURE VERIFICATION was applied to a dataset of SIGNATUREs from Turkish, Chinese and English people. The results of the experiments based on Fisher, Parzen Window and Support Vectors Machine classifications, showed that equal error rate (EER) is 1.46% and 3.51% with universal threshold for random and skilled forgeries, respectively.

Background: With the increasing advancement of technology, it is necessary to develop more accurate, convenient, and cost‑ effective security systems. Handwriting SIGNATURE, as one of the most popular and applicable biometrics, is widely used to register ownership in banking systems, including checks, as well as in administrative and financial applications in everyday life, all over the world. Automatic SIGNATURE VERIFICATION and recognition systems, especially in the case of online SIGNATUREs, are potentially the most powerful and publicly accepted means for personal authentication. Methods: In this article, a novel procedure for online SIGNATURE VERIFICATION and recognition has been presented based on Dual‑ Tree Complex Wavelet Packet Transform (DT‑ CWPT). Results: In the presented method, three‑ level decomposition of DT‑ CWPT has been computed for three time signals of dynamic information including horizontal and vertical positions in addition to the pressure signal. Then, in order to make feature vector corresponding to each SIGNATURE, log energy entropy measures have been computed for each subband of DT‑ CWPT decomposition. Finally, to classify the query SIGNATURE, three classifiers including k‑ nearest neighbor, support vector machine, and Kolmogorov– Smirnov test have been examined. Experiments have been conducted using three benchmark datasets: SVC2004, MCYT‑ 100, as two Latin online SIGNATURE datasets, and NDSD as a Persian SIGNATURE dataset. Conclusion: Obtained favorable experimental results, in comparison with literature, confirm the effectiveness of the presented method in both online SIGNATURE VERIFICATION and recognition objects.

Despite the growing growth of technology, handwritten SIGNATURE has been selected as the first option between biometrics by users. In this paper, a new methodology for offline handwritten SIGNATURE VERIFICATION and recognition based on the Shearlet transform and transfer learning is proposed. Since, a large percentage of handwritten SIGNATUREs are composed of curves and the performance of a SIGNATURE VERIFICATION/recognition system is directly related to the edge structures, subbands of shearlet transform of SIGNATURE images are good candidates for input information to the system. Furthermore, by using transfer learning of some pre-trained models, appropriate features would be extracted. In this study, four pre-trained models have been used: SigNet and SigNet-F (trained on offline SIGNATURE datasets), VGG16 and VGG19 (trained on ImageNet dataset). Experiments have been conducted using three datasets: UTSig, FUM-PHSD and MCYT-75. Obtained experimental results, in comparison with the literature, verify the effectiveness of the presented method in both SIGNATURE VERIFICATION and SIGNATURE recognition.