Automatic Verification System Research Articles

Specification and Verification for Unrestricted Algebraic Effects and Handling

Programming with user-defined effects and effect handlers has many practical use cases involving imperative effects. Additionally, it is natural and powerful to use multi-shot effect handlers for non-deterministic or probabilistic programs that allow backtracking to compute a comprehensive outcome. Existing works for verifying effect handlers are restricted in one of three ways: i) permitting multi-shot continuations under pure setting; ii) allowing heap manipulation for only one-shot continuations; or iii) allowing multi-shot continuations with heap-manipulation but under a restricted frame rule. This work proposes a novel calculus called Effectful Specification Logic (ESL) to support unrestricted effect handlers, where zero-/one-/multi-shot continuations can co-exist with imperative effects and higher-order constructs. ESL captures behaviors in stages, and provides precise models to support invoked effects, handlers and continuations. To show its feasibility, we prototype an automated verification system for this novel specification logic, prove its soundness, report on useful case studies, and present experimental results. With this proposal, we have provided an extended specification logic that is capable of modeling arbitrary imperative higher-order programs with algebraic effects and continuation-enabled handlers.

CNN and Adaboost fusion model for multiface recognition based automated verification system of students attendance

In recent times, companies and institutions globally are increasingly adopting automated systems for recording employee attendance due to the inefficiency and error-prone nature of traditional methods. Face recognition is the fastest, most natural, and most accurate way to identify someone, despite its difficulty. Remote deployment and control of the technology using internet of things (IoT) protocols provides real-time attendance data worldwide. We use the Haar-cascade algorithm to detect and extract features and the adaptive boost algorithm confused with convolutional neural network (CNN) algorithm to recognize the face in our proposed smart attendance system. Per frame, the proposed system recognizes multiple faces. Face recognition in 18 conditions was designed into the proposed system to ensure its versatility. The system's graphical user interface (GUI) was made for average users. This work is more important because IoT technology records student attendance and sends data to authorities. We use Raspberry Pi 4 and camera module for our suggested system. Python and OpenCV libraries tested the multiple face image recognition proposal in 18 situations under four conditions. Single-face image recognition was compared to other methods. In most cases, the proposed method was 100% accurate and outperformed related methods.

Design and Implementation of an Automatic Test and Verification System for SoC

With the development of semiconductor technology in recent years, the design of SoC has become more and more complex. [1]In order to ensure the correctness of chip design and the success rate of chip taping out, the verification test of SoC becomes more and more important. This paper designs an automated test and verification system based on STM32 and analog switch matrix chips. The system has high reusability, good scalability, convenient maintenance and automated test, and can improve the efficiency of test and verification, reduce the cost and shorten the development cycle of SoC.

Fingerprint matching using the onion peeling approach and turning function

Fingerprint, as one of the most popular and robust biometric traits, can be used in automatic identification and verification systems to identify individuals. Fingerprint matching is a vital and challenging issue in fingerprint recognition systems. Most fingerprint matching algorithms are minutiae-based. The minutiae points are the ways that the fingerprint ridges can be discontinuous. Ridge ending and ridge bifurcation are two frequently used minutiae in most fingerprint matching algorithms. This article presents a new minutiae-based fingerprint matching using the onion peeling approach. In the proposed method, fingerprints are aligned to find the matched minutiae points. Then, the nested convex polygons of matched minutiae points are constructed and the comparison between peer-to-peer polygons is performed by the turning function distance. Simplicity, accuracy, and low time complexity of the onion peeling approach are three important factors that make it a standard method for fingerprint matching purposes. The performance of the proposed algorithm is evaluated on the database FVC2002. Since the fingerprints that the difference between the number of their layers is more than 2 and the a minutiae matching score lower than 0.15 are ignored, better results are obtained. KeywordsFingerprint Matching, Minutiae, Convex Layers, Turning Function, Computational Geometry.

Automatic dose verification system for breast radiotherapy: Method validation, contour propagation and DVH parameters evaluation

PurposeImage guided radiotherapy (IGRT) strategies allow detecting and monitoring anatomical changes during external beam radiotherapy (EBRT). However, assessing the dosimetric impact of anatomical changes is not straightforward. In current IGRT strategies dose volume histograms (DVH) are not available due to lack of contours and dose recalculations on the cone-beam CT (CBCT) scan. This study investigates the feasibility of using automatically calculated DVH parameters in CBCTs using an independent dose calculation engine and propagated contours. MethodA prospective study (NCT03385031) of thirty-one breast cancer patients who received additional CBCT imaging (N = 70) was performed. Manual and automatically propagated contours were generated for all CBCTs and an automatic dose recalculation was performed. Differences between planned and CBCT-derived DVH parameters (mean and maximum dose to targets, 95% volume coverage to targets and mean heart dose (MHD)) were calculated using the dose verification system with manual and propagated contours and, in both cases, benchmarked against DVH differences quantified in the TPS using manually contoured CBCTs. ResultsDifferences in DVH parameters between the TPS and dose verification system with propagated contours were −1.3% to 0.7% (95% CI) for mean dose to the target volume, −0.3 to 0.2 Gy (95% CI) in MHD and −3.9% to 2.9% (95% CI) in target volume coverage. ConclusionThe use of an independent fully automatic dose verification system with contour propagation showed to be feasible and sufficiently reliable to recalculate CBCT based DVHs during breast EBRT. Volume coverage parameters, i.e. V95%, proved to be especially sensitive to contouring differences.

An integrated approach on verification of signatures using multiple classifiers (SVM and Decision Tree): A multi-classification approach

A signature is a handwritten representation that is commonly used to validate and recognize the writer individually. An automated verification system is mandatory to verify the identity. The signature essentially displays a variety of dynamics and the static characteristics differ with time and place. Many scientists have already found different algorithms to boost the signature verification system function extraction point. The paper is aimed at multiplying two different ways to solve the problem in digital, manual, or some other means of verifying signatures. The various characteristics of the signature were found through the most adequately implemented methods of machine learning (support vector and decision tree). In addition, the characteristics were listed after measuring the effects. An experiment was performed in various language databases. More precision was obtained from the feature.

Development of automated verification system for structural details of rebar arrangement shop drawings using building information modeling

Development of automated verification system for structural details of rebar arrangement shop drawings using building information modeling

Feature Focus: Towards Explainable and Transparent Deep Face Morphing Attack Detectors

Detecting morphed face images has become an important task to maintain the trust in automated verification systems based on facial images, e.g., at automated border control gates. Deep Neural Network (DNN)-based detectors have shown remarkable results, but without further investigations their decision-making process is not transparent. In contrast to approaches based on hand-crafted features, DNNs have to be analyzed in complex experiments to know which characteristics or structures are generally used to distinguish between morphed and genuine face images or considered for an individual morphed face image. In this paper, we present Feature Focus, a new transparent face morphing detector based on a modified VGG-A architecture and an additional feature shaping loss function, as well as Focused Layer-wise Relevance Propagation (FLRP), an extension of LRP. FLRP in combination with the Feature Focus detector forms a reliable and accurate explainability component. We study the advantages of the new detector compared to other DNN-based approaches and evaluate LRP and FLRP regarding their suitability for highlighting traces of image manipulation from face morphing. To this end, we use partial morphs which contain morphing artifacts in predefined areas only and analyze how much of the overall relevance each method assigns to these areas.

When Borders Blur - Overcoming Political Limits with Computing in Truly Global Societies

As global conditions cause an increasing need for people to move across borders, current gate-keeping mechanisms (e.g. the visa process) make such movements extremely difficult. While it’s understandable that governments want to maintain security within their countries, it is possible for displaced people to contribute economically to development in the destination countries. Even though a completely borderless world of governments would be difficult to achieve, we argue that governments can combine policy with technology to remove barriers to potential growth from migrants, while maintaining security. In this paper, we sketch an interoperable digital government identity and automatic verification systems aimed at allowing governance structures while overcoming the limits of human processing in the visa application system in the face of ecological limits.

The Data Sharing Platform System of Electrical Main Wiring Based on the Results of 3D Digital GIM

The main wiring design is the key to the design of the substation system. It is the process of establishing the logic model of the entire substation. Through the digital design of the main wiring, the whole process of digitization from equipment information to the logic model of the substation and even the layout model of the substation is realized. At the same time, it is a real digital handover Provide the basis. Aiming at the problems of automatic generation and verification, release and query, update and management of the main wiring diagram of the power grid, based on the relevant software of the PMS system, a design method of the integrated drawing management system combining C/S and B/S is presented. Realize the complete synchronization update of the electrical main wiring drawing of the dispatch centre. After the automatic generation and verification system of the main wiring diagram of the power grid was put into operation in the Guangdong Power Grid, it effectively improved the drawing speed, accuracy and standardization of the drawing.

An on-line anomaly identifying method for calibration devices in an automatic verification system for electricity smart meters

Calibration devices in an automatic verification system are used to verify the accuracy of electricity smart meters. As time goes by, calibration devices may experience the metering performance degradation, making results of error tests biased. Therefore, it is of great significance to ensure calibration devices have qualified metering performance. Conventionally, their performance could only be manually inspected at regular intervals, while keep unknown during the operation. To address this issue, an on-line anomaly identifying method based on calibration devices’ parallel working mode is proposed to check their metering performance in real time. Instead of off-line inspections, a device’s metering performance is associated with the corresponding results of error tests, and comparisons among distributions based on results corresponding to different calibration devices would provide evidences to identify the ones having metering performance unqualified without interrupting the verification tasks. In addition, a case study is conducted to prove the validity.

An Improved Vehicle Registration and Licensing System

Vehicle registration and licensing systems have been in existence for decades. However, there has been over 55% increase in the number of reported stolen vehicles which have not been recovered in the last 3 years. Therefore, there is need to improve on its existing systems by incorporating anti-theft capability of vehicles using fingerprint biometric technology. In developing the Secured Vehicle Registration System (SVRS), data on vehicle registration, renewal and change of ownership procedures were collected from the Motor Licensing Office at Osogbo. Unified Modelling Language (UML) tools was used for the system design, to illustrate the whole system in a clearer way, and implemented using JavaScript, PHP scripting language, HTML, XAMPP SQLServer and the Mantra MFS100 fingerprint scanner. The system performance was evaluated by authenticating registered vehicle owner biometric to calculate the Accuracy, Average Response Time (ART) and Apdex Score. The result of the evaluation using 288 fingerprint templates of 72 vehicle owners shows the False Accept Rate (FAR) of 0.0% and False Reject Rate (FRR) of 2.1%, which is equivalent to a system accuracy of 97.9%. The ART for the fingerprint execution matching is 36.3 microseconds while the overall system satisfaction Apdex score recorded was 0.73, which denotes a satisfying system. The high-speed verification method uses the lowest computational power and execution time to deliver accurate results through making a match or non-match against stored templates. The developed system demonstrated its ability to link vehicle(s) to its respective owner(s) and also function as an automatic identity verification system for vehicle owners using VIN and fingerprint. The system has the ability to be employed for preventing fraudulent change of ownership and also help reduce delay in processing vehicle license.Keywords— authentication, biometrics, database, fraudulent, information protection, motor vehicle administration, motor vehicle registry, security code, vehicle owners

Off-line Handwritten Signature Verification System: Artificial Neural Network Approach

Nowadays, it is evident that signature is commonly used for personal verification, this justifies the necessity for an Automatic Verification System (AVS). Based on the application, verification could either be achieved Offline or Online. An online system uses the signature’s dynamic information; such information is captured at the instant the signature is generated. An offline system, on the other hand, uses an image (the signature is scanned). In this paper, some set of simple shaped geometric features are used in achieving offline Verification of signatures. These features include Baseline Slant Angle (BSA), Aspect Ratio (AR), and Normalized Area (NA), Center of Gravity as well as the line’s Slope that joins the Center of Gravities of the signature’s image two splits. Before the features extraction, a signature preprocessing is necessary to segregate its parts as well as to eliminate any available spurious noise. Primarily, System training is achieved via a signature record which was acquired from personalities whose signatures had to be validated through the system. An average signature is acquired for each subject as a result of incorporating the aforementioned features which were derived from a sample set of the subject’s true signatures. Therefore, a signature functions as the prototype for authentication against a requested test signature. The similarity measure within the feature space between the two signatures is determined by Euclidian distance. If the Euclidian distance is lower than a set threshold (i.e. analogous to the minimum acceptable degree of similarity), the test signature is certified as that of the claiming subject otherwise detected as a forgery. Details on the stated features, pre-processing, implementation, and the results are presented in this work.

Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation

Usually, in a real-world scenario, few signature samples are available to train an automatic signature verification system (ASVS). However, such systems do indeed need a lot of signatures to achieve an acceptable performance. Neuromotor signature duplication methods and feature space augmentation methods may be used to meet the need for an increase in the number of samples. Such techniques manually or empirically define a set of parameters to introduce a degree of writer variability. Therefore, in the present study, a method to automatically model the most common writer variability traits is proposed. The method is used to generate offline signatures in the image and the feature space and train an ASVS. We also introduce an alternative approach to evaluate the quality of samples considering their feature vectors. We evaluated the performance of an ASVS with the generated samples using three well-known offline signature datasets: GPDS, MCYT-75, and CEDAR. In GPDS-300, when the SVM classifier was trained using one genuine signature per writer and the duplicates generated in the image space, the Equal Error Rate (EER) decreased from 5.71% to 1.08%. Under the same conditions, the EER decreased to 1.04% using the feature space augmentation technique. We also verified that the model that generates duplicates in the image space reproduces the most common writer variability traits in the three different datasets.

Specimen Authentication System Using Pixel-Based Algorithm

Specimen Authentication is the process used to recognize an individual's handwritten signature. The pre-process of verifying a signature is Pixel-based. There is a need for an automatic verification system for a signature since the signature has been a means of a person's identification for ages. Offline systems work on the scanned image of a signature. The main objective of the study was to design and develop a Specimen Authentication System to examine signatures. In this study, the researchers verify the off-line signatures by taking a boundary of the entire signature and make the pixel comparison. Signature is acquired using a scanner. The detection process is done after the data acquisition and pre-processing. Pre-processing includes noise removal, grey-scale, manipulation, edge detection. Experimental results show that 50% of the accurate matching with the existing one. There is an important distinction between simple signature Comparisons and dynamic signature verification. Both can be computerized, but a Simple comparison only takes into account what the signature looks like. Dynamic signature verification takes into account how the signature was made. With dynamic signature verification, it is not the shape or looks of the meaningful signature; it is the change in speed, pressure, and timing that occur during the act of signing. Consistency created by natural motion and practice over time creates a recognizable pattern that makes the hand-written signature a natural for biometric identification.

Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures.

We aim at enhancing personal identity security on mobile touch-screen sensors by augmenting handwritten signatures with specific additional information at the enrollment phase. Our former works on several available and private data sets acquired on different sensors demonstrated that there are different categories of signatures that emerge automatically with clustering techniques, based on an entropy-based data quality measure. The behavior of such categories is totally different when confronted to automatic verification systems in terms of vulnerability to attacks. In this paper, we propose a novel and original strategy to reinforce identity security by enhancing signature resistance to attacks, assessed per signature category, both in terms of data quality and verification performance. This strategy operates upstream from the verification system, at the sensor level, by enriching the information content of signatures with personal handwritten inputs of different types. We study this strategy on different signature types of 74 users, acquired in uncontrolled mobile conditions on a largely deployed mobile touch-screen sensor. Our analysis per writer category revealed that adding alphanumeric (date) and handwriting (place) information to the usual signature is the most powerful augmented signature type in terms of verification performance. The relative improvement for all user categories is of at least 93% compared to the usual signature.

Segmentation of Certificate With Connected Component Labeling Method

Certificate is one of the documents that can be used as evidence of ownership or an event. For example, when certificate used as requirement to participate in an event. If a document is made as a requirement, of course the file verification process will be done. Seeing the time optimization problem when verifying the file, the authors carry out research by segmenting important data contained in a certificate as an initial step in the development of an automatic document verification system. The segmentation process carried out in this study uses the Connected Component Labeling method in determining the area to be segmented and Automatic Cropping to cut the results of the segmentation process. By using these two methods obtained an accuracy of 60% with a total of 15 pieces of test data

Research on Intelligent Operation and Maintenance Platform Based on Automatic Calibration System of Electric Energy Meter

To the problem that the operation and maintenance of the electric energy meter automatic verification system is large and the equipment fault location not accurate, a smart operation and maintenance platform is developed. The function and structure of the system are analyzed, and the application effect of the intelligent operation and maintenance platform is demonstrated. The actual operation results show that: The application of intelligent operation and maintenance platform improves the operation and maintenance efficiency of the energy meter automatic verification system, which produces a good economic and social benefits.

Online Handwritten Signature Verification and Recognition Based on Dual-Tree Complex Wavelet Packet Transform.

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.

Writer Independent Manipuri Offline Signature Verification using Transfer Learning Technique of Convolutional Neural Network

Automatic Signature Verification system is used to verify whether a signature is genuine or forged. Forged Signatures are those signatures that a person produced by imitating the signature of another person. Automatic Signature Verification is very important as a person’s handwritten signature is used everywhere to authenticate themselves and there is not very much difference between a genuine signature and the imitation of it, i.e. a forged signature. In this work, signature verification is done using different pre-trained Convolutional Neural Networks (CNNs). Convolutional Neural Network has powerful learning ability, and it can be used to distinguish between a genuine and a forged signature automatically. In this experiment, Manipuri signature dataset was used, the dataset was prepared originally and it contains 729 genuine signatures and 243 forged signatures. Features were extracted from pre-trained networks and classification was done using binary Support Vector Machine (SVM) classifier and the performances of the networks were compared. And according to the experiment we achieved a classification accuracy of 84.7 using VGG19 features, accuracy of 86.8 using VGG16 features and accuracy of 81.9 using Alexnet features.