Automatic Verification System Research Articles

Automatic verification and data processing system of indicator based on grating principle

The rapid progress of modern science and technology promotes the continuous development of precision measurement technology and improves the accuracy and efficiency of measuring instruments. In optical precision measurement technology, grating measurement technology has the advantage of high precision and large range. This study introduces the principle, structure and data handling of grating tester for dial indicator gauges. As the grating tester for dial indicator gauges is a kind of high precision measuring standard instrument, the verification basis, verification method and uncertainty analysis are also discussed in depth in this study.

Privacy-preserving PLDA speaker verification using outsourced secure computation

The usage of biometric recognition has become prevalent in various verification processes, ranging from unlocking mobile devices to verifying bank transactions. Automatic speaker verification (ASV) allows an individual to verify its identity towards an online service provider by comparing freshly sampled speech data to reference information stored on the service provider’s server. Due to the sensitive nature of biometric data, the storage and usage thereof is subject to recent EU regulations introduced as means to protect the privacy of individuals enrolled in an automatic biometric verification system. Stored biometric data need to be unlinkable, irreversible, and renewable to satisfy international standards. Preserving privacy in ASV is also important because, contrary to other biometric characteristics such as fingerprints, speech data can be used to infer a lot of sensitive information about the data subject. As a result, some architectures have been proposed to enable privacy-preserving ASV in the encrypted domain. Recently, homomorphic encryption (HE) was proposed to protect both subject features and vendor models in an embedding-based ASV. This architecture improves on previous privacy-preserving ASV by using (probabilistic) embeddings (i-vectors) and by additionally protecting the vendor’s model. However, the usage of HE comes with a rather heavy overhead and significantly slows down the verification process.In this work, we align the cryptographic notion of outsourced secure two-party computation to embedding-based ASV. Our architecture protects biometric information in ASV and can also be used for any automatic biometric verification task. We show that unlinkability, irreversibility, and renewability are granted. Compared to the HE solution, our architecture results in considerably lower communication and computation overhead. Our architecture has been implemented and is experimentally evaluated on the NIST i-vector challenge 2014 using the cosine distance and log-likelihood ratio (LLR) scores from probabilistic linear discriminant analysis (PLDA) and two-covariance (2Cov) comparators. The results show that verification accuracy is retained while efficiency is improved. For instance, a PLDA verification with an embedding dimension of 200 takes about 77 milliseconds over a LAN. This is an improvement of more than 3000 × over the HE-based solution and shows that privacy of subject and vendor data can be preserved in ASV while retaining practical verification times. Moreover, our system is secure against malicious client devices.

ASSV

As one kind of biological characteristics of people, handwritten signature has been widely used in the banking industry, government and education. Verifying handwritten signatures manually causes too much human cost, and its high probability of errors can threaten the property safety and even society stability. Therefore, the need for an automatic verification system is emphasized. This paper proposes a device-free on-line handwritten signature verification system ASSV, providing paper-based handwritten signature verification service. As far as we know, ASSV is the first system which uses the changes of acoustic signals to realize signature verification. ASSV differs from previous on-line signature verification work in two aspects: 1. It requires neither a special sensor-instrumented pen nor a tablet; 2. People do not need to wear a device such as a smartwatch on the dominant hand for hand tracking. Differing from previous acoustic-based sensing systems, ASSV uses a novel chord-based method to estimate phase-related changes caused by tiny actions. Then based on the estimation, frequency-domain features are extracted by a discrete cosine transform (DCT). Moreover, a deep convolutional neural network (CNN) model fed with distance matrices is designed to verify signatures. Extensive experiments show that ASSV is a robust, efficient and secure system achieving an AUC of 98.7% and an EER of 5.5% with a low latency.

Automatic detection of electric meter position in automated assembly line turnover box

Abstract: In the process of metering and automatic production system in the measurement and verification of the loading robot, if there is a positional shift of the electric meter, the gripper of the loading robot will mechanically collide with the electric meter. Due to the large force of the loading robot, the appearance of the electric meter is damaged, and the mechanical arm of the loading robot is broken, which reduces the service life of the measuring and verifying equipment, which is not conducive to the continuous and stable operation of the automatic verification system. In this paper, an automatic detection system of electric meter position in the turnaround box is proposed, which can reduce the failure rate of the automatic verification system, improve the verification efficiency, reduce the loss of metering assets and the occurrence of safety accidents by realizing the offset detection and correction of the electric meter position in the turnover box.

Research on Verification Algorithm and Risk Identification Method of Electric Power Measurement

This paper studies quality verification algorithm and risk identification method of automated verification of electric energy meter under the measurement intensive mode, including dynamic and static verification algorithm system of verification quality, and realizes automated assembly line operation and automated acquisition, conversion and analysis of status information data by ETL, OPC and other technologies, semi-quantitatively describes software and hardware, human error in the automated verification assembly line operation and risk identification method indicators for all kinds of risk critical equipment caused by external system failures in combination with the risk matrix method. The dynamic quality verification can be used to form transverse comparison for the verification sources in the verification equipment, the static quality verification can be used to vertically compare different batches of information tables, the equipment status acquisition and risk matrix algorithm can be used to effectively make the line operation state index to provide managerial personnel with auxiliary decision basis, thereby, the verification quality, the stability and reliability of the line operation of automated verification system can be effectively guaranteed.

Applications of the Whittaker smoother in NMR spectroscopy

This review presents a general survey of the main applications of the Whitttaker Smoother in 1D and 2D NMR spectroscopy, including smoothing, interpolation, solvent suppression and baseline correction. Juan Carlos Cobas Gómez, Ph.D. Dr. Carlos Cobas is co-founder, President of Mestrelab Research and director of the Research and Development department at Mestrelab Research. He was born in Santiago de Compostela, Spain, in 1971. He received his B.Sc. in Chemistry from the University of Santiago de Compostela. After spending a year at the University of Leicester (U.K.) under the supervision of Dr. Paul R. Jenkins, he returned to the University of Santiago, where he obtained a M.Sc. degree in 1995 and a Ph.D. in 2000. At the end of 2004, after 8 years working on the MestReC project, he took the decision to spin off from the Universidad de Santiago de Compostela and launch Mestrelab Research. His research activities cover diverse applications of NMR spectroscopy, from signal processing and analysis algorithms to quantum simulation of NMR data, as well as methodology and applications of automatic structure verification systems by NMR and LC/GC/MS. He currently has a particular focus on the application of machine learning techniques in the fields of spectroscopy and chemoinformatics.

Leto: verifying application-specific hardware fault tolerance with programmable execution models

Researchers have recently designed a number of application-specific fault tolerance mechanisms that enable applications to either be naturally resilient to errors or include additional detection and correction steps that can bring the overall execution of an application back into an envelope for which an acceptable execution is eventually guaranteed. A major challenge to building an application that leverages these mechanisms, however, is to verify that the implementation satisfies the basic invariants that these mechanisms require---given a model of how faults may manifest during the application's execution. To this end we present Leto, an SMT-based automatic verification system that enables developers to verify their applications with respect to an execution model specification. Namely, Leto enables software and platform developers to programmatically specify the execution semantics of the underlying hardware system as well as verify assertions about the behavior of the application's resulting execution. In this paper, we present the Leto programming language and its corresponding verification system. We also demonstrate Leto on several applications that leverage application-specific fault tolerance

From BoVW to VLAD with KAZE features: Offline signature verification considering cognitive processes of forensic experts

The widespread use of handwritten signatures for identity authentication has resulted in a need for automated verification systems. However, there is room for improvement in the performance of automated systems compared to human analysis, particularly that of forensic document examiners (FDEs), under a wide range of conditions. As any inaccuracy in analysis can cause serious problems, further research is required to improve performance. In this study, to improve the performance of offline signature verification under various writing conditions, new approaches using feature encoding methods of a bag-of-visual words (BoVW) and a vector of locally aggregated descriptors (VLAD) are adopted. The proposed method considers current knowledge about the cognitive processes used by FDEs to improve performance. The proposed method incorporates an approach based on BoVW and VLAD with local features to mimic the cognitive processes of FDEs for feature extraction. In addition, KAZE features detected in strokes and background space are employed as local features to enhance discriminative power. The performance of the proposed approach is demonstrated using the publicly-available CEDAR and MCYT-75 signature datasets.

Towards the design of an offline signature verifier based on a small number of genuine samples for training

Signature verification has remained one of the most widely accepted modalities to authenticate an individual primarily due to the ease with which signatures can be acquired. Being a behavioral biometric modality, the intra-personal variability in signatures is rather high and extremely unpredictable. This leads to relatively higher error rates as compared to those realized by other biometric traits like iris or fingerprints. To address these issues, this study investigates run-length distribution features for designing an effective offline signature verification system. The objective of this research is to enhance the capabilities of automatic signature verification systems allowing them to work in a realistic fashion by training them using positive specimens (genuine signatures of each person) only without access to any forged samples. Classification is carried out using One-Class Support Vector Machine (OC-SVM) while the evaluations are performed using GPDS960 database, one of the largest offline signature corpus developed till date. Experimental results show the potential of the proposed system for detection of skilled forgeries, especially for the challenging case of a single reference signature in the training set.

Analysis and Design of a Novel Integrated Automatic Verification System Software Platform

Analysis and Design of a Novel Integrated Automatic Verification System Software Platform

Static and Dynamic Synthesis of Bengali and Devanagari Signatures.

Developing an automatic signature verification system is challenging and demands a large number of training samples. This is why synthetic handwriting generation is an emerging topic in document image analysis. Some handwriting synthesizers use the motor equivalence model, the well-established hypothesis from neuroscience, which analyses how a human being accomplishes movement. Specifically, a motor equivalence model divides human actions into two steps: 1) the effector independent step at cognitive level and 2) the effector dependent step at motor level. In fact, recent work reports the successful application to Western scripts of a handwriting synthesizer, based on this theory. This paper aims to adapt this scheme for the generation of synthetic signatures in two Indic scripts, Bengali (Bangla), and Devanagari (Hindi). For this purpose, we use two different online and offline databases for both Bengali and Devanagari signatures. This paper reports an effective synthesizer for static and dynamic signatures written in Devanagari or Bengali scripts. We obtain promising results with artificially generated signatures in terms of appearance and performance when we compare the results with those for real signatures.

BIM ORIENTATION: GRADES OF GENERATION AND INFORMATION FOR DIFFERENT TYPE OF ANALYSIS AND MANAGEMENT PROCESS

Abstract. Architecture, Engineering and Construction (AEC) industry is facing a great process re-engineering of the management procedures for new constructions, and recent studies show a significant increase of the benefits obtained through the use of Building Information Modelling (BIM) methodologies. This innovative approach needs new developments for information and communication technologies (ICT) in order to improve cooperation and interoperability among different actors and scientific disciplines. Accordingly, BIM could be described as a new tool capable of collect/analyse a great quantity of information (Big data) and improve the management of building during its life of cycle (LC). The main aim of this research is, in addition to a reduction in production times, reduce physical and financial resources (economic impact), to demonstrate how technology development can support a complex generative process with new digital tools (modelling impact). This paper reviews recent BIMs of different historical Italian buildings such as Basilica of Collemaggio in L’Aquila, Masegra Castle in Sondrio, Basilica of Saint Ambrose in Milan and Visconti Bridge in Lecco and carries out a methodological analysis to optimize output information and results combining different data and modelling techniques into a single hub (cloud service) through the use of new Grade of Generation (GoG) and Information (GoI) (management impact). Finally, this study shows the need to orient GoG and GoI for a different type of analysis, which requires a high Grade of Accuracy (GoA) and an Automatic Verification System (AVS ) at the same time.

Automatic Verification System for Flatness Error of Large Flat

This paper introduces the detection method of flatness error of flat plate, designs a large-scale flatness error automatic verification system. The structure, working principle and flatness error of least square method are described in detail, the uncertainty is analyzed. The advantages of the system are compared and analyzed by experiments, and it has a good application value.

Design of a Multi-station Low-voltage Current Transformer Automatic Verification System

Manual mode is mostly adopted for traditional verification work aiming at low-voltage current transformer performance. Test is completed on many stations. Instruments and equipment should be manually moved, combined, wired and disconnected for many times, and certificate should be frequently issued, etc., thereby consuming a lot of manpower and material resource with high labor intensity and low work efficiency. In the paper, a multi-station low-voltage current transformer automatic verification system design plan is proposed aiming at the disadvantages of manual verification mode. The low-voltage current transformer is tested comprehensively through transformer comprehensive test control cabinet and transformer comprehensive testing bench. It is characterized by convenient use and high operation efficiency.

Protection of Privacy in Biometric Data

Biometrics is commonly used in many automated verification systems offering several advantages over traditional verification methods. Since biometric features are associated with individuals, their leakage will violate individuals’ privacy, which can cause serious and continued problems as the biometric data from a person are irreplaceable. To protect the biometric data containing privacy information, a number of privacy-preserving biometric schemes (PPBSs) have been developed over the last decade, but they have various drawbacks. The aim of this paper is to provide a comprehensive overview of the existing PPBSs and give guidance for future privacy-preserving biometric research. In particular, we explain the functional mechanisms of popular PPBSs and present the state-of-the-art privacy-preserving biometric methods based on these mechanisms. Furthermore, we discuss the drawbacks of the existing PPBSs and point out the challenges and future research directions in PPBSs.

Hybrid Feature Based Face Verification and Recognition System Using Principal Component Analysis and Artificial Neural Network

In Human Computer Interaction (HCI), the issue of developing an automatic face verification and recognition system has been one of the most important concerns of the researchers due to its wide range of application especially in the areas where there has been a high demand for consistent identification electronic access system for an individual. This paper discusses an approach to verify the human face and recognize the person's face through a still image. The proposed method is a hybrid approach that considers the local components of the face as well as the entire face of a human being. The local facial components comprises of the lips, nose, left eye and right eye. The proposed system has been implemented using Principal Component Analysis (PCA) and the Artificial Neural Network (ANN). The system has been designed to handle the noises, illumination variations and the facial emotions to some extent. Hence, the proposed system proves to be efficient as it gives the correct recognition rate of 93.5% for ideal facial image and approximately 85% for noise affected facial image.

Online and Offline Signature Verification: A Combined Approach

Handwritten signature verification is an emerging area. In this paper, an automatic signature verification system has been proposed. This work focuses on both online and offine features of handwritten signatures and aims at combining their results to verify the signature. Signatures are collected for both online and offine. Online data collected is the signing process captured using a webcam and offine data collected are the scanned signatures. Initially both data undergoes appropriate preprocessing steps. Then feature extraction is done where features based on pen tip tracking are used in case of online and gradient and projection based features are used in case of offine method. Later the online and offine method verifies the signature separately and finally their results are combined and the signature is verified using SVM. Paper also compares the results of online, offine and combined approach.

The Influence of Template Ageing on a Dynamic Signature Verification System

This paper addresses template ageing in automatic signature verification systems. Handwritten signatures are a behavioral biometric sensitive to the passage of time. The experiments in this paper utilized a database that contains signature realizations captured in three sessions. The last session was captured seven years after the first one. The results presented in this paper show a potential risk of using an automatic handwriting verification system without including template ageing
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Mobile signature verification: feature robustness and performance comparison

In this study, the effects of using handheld devices on the performance of automatic signature verification systems are studied. The authors compare the discriminative power of global and local signature features between mobile devices and pen tablets, which are the prevalent acquisition device in the research literature. Individual feature discriminant ratios and feature selection techniques are used for comparison. Experiments are conducted on standard signature benchmark databases (BioSecure database) and a state-of-the-art device (Samsung Galaxy Note). Results show a decrease in the feature discriminative power and a higher verification error rate on handheld devices. It is found that one of the main causes of performance degradation on handheld devices is the absence of pen-up trajectory information (i.e. data acquired when the pen tip is not in contact with the writing surface).

Hand vein authentication using biometric graph matching

This study proposes an automatic dorsal hand vein verification system using a novel algorithm called biometric graph matching (BGM). The dorsal hand vein image is segmented using the K-means technique and the region of interest is extracted based on the morphological analysis operators and normalised using adaptive histogram equalisation. Veins are extracted using a maximum curvature algorithm. The locations and vascular connections between crossovers, bifurcations and terminations in a hand vein pattern define a hand vein graph. The matching performance of BGM for hand vein graphs is tested with two cost functions and compared with the matching performance of two standard point patterns matching algorithms, iterative closest point (ICP) and modified Hausdorff distance. Experiments are conducted on two public databases captured using far infrared and near infrared (NIR) cameras. BGM's matching performance is competitive with state-of-the-art algorithms on the databases despite using small and concise templates. For both databases, BGM performed at least as well as ICP. For the small sized graphs from the NIR database, BGM significantly outperformed point pattern matching. The size of the common subgraph of a pair of graphs is the most significant discriminating measure between genuine and imposter comparisons.