Non-english and non-latin signature verification systems: A survey

Signatures continue to be an important biometric because they remain widely used as a means of personal verification and therefore an automatic verification system is needed. Manual signature-based authentication of a large number of documents is a difficult and time consuming task. Consequently for many years, in the field of protected communication and financial applications, we have observed an explosive growth in biometric personal authentication systems that are closely connected with measurable unique physical characteristics (e.g. hand geometry, iris scan, finger prints or DNA) or behavioural features. Substantial research has been undertaken in the field of signature verification involving English signatures, but to the best of our knowledge, very few works have considered non-English signatures such as Chinese, Japanese, Arabic etc. In order to convey the state-of-the-art in the field to researchers, in this paper we present a survey of non-English and non-Latin signature verification systems

Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks

Signature is widely used and developed area of research for personal verification and authentication. In this paper Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks (SVFGNN) is presented. The global and grid features are fused to generate set of features for the verification of signature. The test signature is compared with data base signatures based on the set of features and match/non match of signatures is decided with the help of Neural Network. The performance analysis is conducted on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm

Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour

Authentication is very much essential in managing security. In modern times, it is one in all priorities. With the advent of technology, dialogue with machines becomes automatic. As a result, the need for authentication for a variety of security purposes is rapidly increasing. For this reason, biometrics-based certification is gaining dramatic momentum. The proposed method describes an off-line Genuine/ Forgery signature classification system using radon transform and K-Nearest Neighbour classifier. Every signature features are extracted by radon transform and they are aligned to get the statistic information of his signature. To align the two signatures, the algorithm used is Extreme Points Warping. Many forged and genuine signatures are selected in K-Nearest Neighbour classifier training. By aligning the test signature with each and every reference signatures of the user, verification of test signature is done. Then the signature can be found whether it is genuine or forgery. A K-Nearest Neighbour is used for classification for the different datasets. The result determines how the proposed procedure is exceeds the current state-of-the-art technology. Approximately, the proposed system’s performance is 90 % in signature verification system

Multimodal biometrics scheme based on discretized eigen feature fusion for identical twins identification

The subject of twins multimodal biometrics identification (TMBI) has consistently been an interesting and also a valuable area of study. Considering high dependency and acceptance, TMBI greatly contributes to the domain of twins identification in biometrics traits. The variation of features resulting from the process of multimodal biometrics feature extraction determines the distinctive characteristics possessed by a twin. However, these features are deemed as inessential as they cause the increase in the search space size and also the difficulty in the generalization process. In this regard, the key challenge is to single out features that are deemed most salient with the ability to accurately recognize the twins using multimodal biometrics. In identification of twins, effective designs of methodology and fusion process are important in assuring its success. These processes could be used in the management and integration of vital information including highly selective biometrics characteristic possessed by any of the twins. In the multimodal biometrics twins identification domain, exemplification of the best features from multiple traits of twins and biometrics fusion process remain to be completely resolved. This research attempts to design a new scheme and more effective multimodal biometrics twins identification by introducing the Dis-Eigen feature-based fusion with the capacity in generating a uni-representation and distinctive features of numerous modalities of twins. First, Aspect United Moment Invariant (AUMI) was used as global feature in the extraction of features obtained from the twins handwritingfingerprint shape and style. Then, the feature-based fusion was examined in terms of its generalization. Next, to achieve better classification accuracy, the Dis-Eigen feature-based fusion algorithm was used. A total of eight distinctive classifiers were used in executing four different training and testing of environment settings. Accordingly, the most salient features of Dis-Eigen feature-based fusion were trained and tested to determine the accuracy of the classification, particularly in terms of performance. The results show that the identification of twins improved as the error of similarity for intra-class decreased while at the same time, the error of similarity for inter-class increased. Hence, with the application of diverse classifiers, the identification rate was improved reaching more than 93%. It can be concluded from the experimental outcomes that the proposed method using Receiver Operation Characteristics (ROC) considerably increases the twins handwriting-fingerprint identification process with 90.25% rate of identification when False Acceptance Rate (FAR) is at 0.01%. It is also indicated that 93.15% identification rate is achieved when FAR is at 0.5% and 98.69% when FAR is at 1.00%. The new proposed solution gives a promising alternative to twins identification application

Enhancement of finger vein image using multifiltering algorithm

Finger vein images are prone to various noise due to imperfect capturing device used. As a result of transmission errors, pixel elements in the capturing sensors malfunction, memory location fault and analog-to-digital conversion time errors, noise could also occur. Using single filtering method cannot give a satisfactory result of the vein image. Hence, this paper proposed a multifilter algorithm that's capable of locating the region of interest of the vein and de-noise the image from corrupted noises while the edges of the image are kept intact. The algorithm does not need to have prior knowledge of the image and the corrupted noise. The fusion of simple mask filter algorithm and mixed filter algorithm of Median and Wiener filters is used. Experimental results coded in MATLAB using SDUMLA-HMT finger vein database show that the proposed algorithm is feasible and efficient

Multi-feature approach for writer-independent offline signature verification

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

Handwritten Arabic Digit Recognition Using Convolutional Neural Network

In Computer vision systems, computer vision works by imitating humans in their vision way which is known as the human vision system (HVS). In HVS, humans use their eyes and brains in order to see and classify any object around them. Hence, computer vision systems imitate HSV by developing several algorithms for classifying images and objects. The main goal of this paper is to propose a model for identifying and classifying the Arabic handwritten digits with high accuracy.  The concept of deep learning via the convolutional neural network (CNN) with the ADBase database is used to achieve the goal. The training is done by having a 3*3 and 5*5 filters. Basically, while the classification phase distinct learning rates are used to train the network. The obtained results are encouraging and promising

Asymmetric Image Retrieval with Cross Model Compatible Ensembles

The asymmetrical retrieval setting is a well suited solution for resource constrained applications such as face recognition and image retrieval. In this setting, a large model is used for indexing the gallery while a lightweight model is used for querying. The key principle in such systems is ensuring that both models share the same embedding space. Most methods in this domain are based on knowledge distillation. While useful, they suffer from several drawbacks: they are upper-bounded by the performance of the single best model found and cannot be extended to use an ensemble of models in a straightforward manner. In this paper we present an approach that does not rely on knowledge distillation, rather it utilizes embedding transformation models. This allows the use of N independently trained and diverse gallery models (e.g., trained on different datasets or having a different architecture) and a single query model. As a result, we improve the overall accuracy beyond that of any single model while maintaining a low computational budget for querying. Additionally, we propose a gallery image rejection method that utilizes the diversity between multiple transformed embeddings to estimate the uncertainty of gallery images