Relevant bibliographies by topics / Multi-modal verification systems

Academic literature on the topic 'Multi-modal verification systems'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Multi-modal verification systems"

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Toledano, Doroteo T., Rubén Fernández Pozo, Álvaro Hernández Trapote, and Luis Hernández Gómez. "Usability evaluation of multi-modal biometric verification systems." Interacting with Computers 18, no. 5 (September 2006): 1101–22. http://dx.doi.org/10.1016/j.intcom.2006.01.004.

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Blasco, Jorge, and Pedro Peris-Lopez. "On the Feasibility of Low-Cost Wearable Sensors for Multi-Modal Biometric Verification." Sensors 18, no. 9 (August 24, 2018): 2782. http://dx.doi.org/10.3390/s18092782.

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Biometric systems designed on wearable technology have substantial differences from traditional biometric systems. Due to their wearable nature, they generally capture noisier signals and can only be trained with signals belonging to the device user (biometric verification). In this article, we assess the feasibility of using low-cost wearable sensors—photoplethysmogram (PPG), electrocardiogram (ECG), accelerometer (ACC), and galvanic skin response (GSR)—for biometric verification. We present a prototype, built with low-cost wearable sensors, that was used to capture data from 25 subjects while seated (at resting state), walking, and seated (after a gentle stroll). We used this data to evaluate how the different combinations of signals affected the biometric verification process. Our results showed that the low-cost sensors currently being embedded in many fitness bands and smart-watches can be combined to enable biometric verification. We report and compare the results obtained by all tested configurations. Our best configuration, which uses ECG, PPG and GSR, obtained 0.99 area under the curve and 0.02 equal error rate with only 60 s of training data. We have made our dataset public so that our work can be compared with proposals developed by other researchers.
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Medjahed, Chahreddine, Abdellatif Rahmoun, Christophe Charrier, and Freha Mezzoudj. "A deep learning-based multimodal biometric system using score fusion." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (March 1, 2022): 65. http://dx.doi.org/10.11591/ijai.v11.i1.pp65-80.

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Recent trends in artificial intelligence tools-based biometrics have overwhelming attention to security matters. The hybrid approaches are motivated by the fact that they combine mutual strengths and they overcome their limitations. Such approaches are being applied to the fields of biomedical engineering. A biometric system uses behavioural or physiological characteristics to identify an individual. The fusion of two or more of these biometric unique characteristics contributes to improving the security and overcomes the drawbacks of unimodal biometric-based security systems. This work proposes efficent multimodal biometric systems based on matching score concatenation fusion of face, left and right palm prints. Multimodal biometric identification systems using convolutional neural networks (CNN) and k-nearest neighbors (KNN) are proposed and trained to recognize and identify individuals using multi-modal biometrics scores. Some popular biometrics benchmarks such as FEI face dataset and IITD palm print database are used as raw data to train the biometric systems to design a strong and secure verification/identification system. Experiments are performed on noisy datasets to evaluate the performance of the proposed model in extreme scenarios. Computer simulation results show that the CNN and KNN multi-modal biometric system outperforms most of the most popular up to date biometric verification techniques.
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Soltane, Mohamed. "Figueiredo-Jain (FJ) Tune Algorithm for Gaussian Mixture Modal (GMM) Based Face and Signature Multi-Modal Biometric Verification Fusion Systems." Journal of Computational Intelligence and Electronic Systems 4, no. 1 (March 1, 2015): 27–36. http://dx.doi.org/10.1166/jcies.2015.1110.

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Stanojević,, Milan, Ivan Milenković, Dušan Starčević, and Bogdana Stanojević. "Continuous Distribution Approximation and Thresholds Optimization in Serial Multi-Modal Biometric Systems." International Journal of Computers Communications & Control 11, no. 5 (September 1, 2016): 720. http://dx.doi.org/10.15837/ijccc.2016.5.2683.

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Multi-modal biometric verification systems use information from several biometric modalities to verify an identity of a person. The false acceptance rate (FAR)and false rejection rate (FRR) are metrics generally used to measure the performance of such systems.In this paper, we first approximate the score distributions of both genuine users and impostors by continuous distributions. Then we incorporate the exact expressions of the distributions in the formulas for the expected values of both FAR and FRR for each matcher. In order to determine the upper and lower acceptance thresholds in the sequential multi-modal biometric matching, we further minimize the expected values of FAR and FRR for the entire processing chain. We propose a non-linear bi-objective programming problem whose objective functions are the two error probabilities. We analyze the efficient set of the bi-objective problem, and derive an efficient solution as a best compromise between the error probabilities. Replacing the least squares approximation of the score distributions by a continuous distributionapproximation, this approach modifies the method presented in Stanojević et al. [15] (doi: 10.1109/ICCCC.2016.7496752) (a).The results of our experiments showed a good performance of the sequential multiple biometric matching system based on continuous distribution approximation and optimized thresholds.(a)Reprinted (partial) and extended, with permission based on License Number3938230385072 © [2016] IEEE, from "Computers Communications and Control (ICCCC), 2016 6th International Conference on".
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Norah Abdullah Al-johani and Lamiaa A. Elrefaei. "Palmprint And Dorsal Hand Vein Multi-Modal Biometric Fusion Using Deep Learning." International Journal of Artificial Intelligence and Machine Learning 10, no. 2 (July 2020): 18–42. http://dx.doi.org/10.4018/ijaiml.2020070102.

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Advancements in biometrics have attained relatively high recognition rates. However, the need for a biometric system that is reliable, robust, and convenient remains. Systems that use palmprints (PP) for verification have a number of benefits including stable line features, reduced distortion and simple self-positioning. Dorsal hand veins (DHVs) are distinctive for every person, such that even identical twins have different DHVs. DHVs appear to maintain stability over time. In the past, different features algorithms were used to implement palmprint (PP) and dorsal hand vein (DHV) systems. Previous systems relied on handcrafted algorithms. The advancements of deep learning (DL) in the features learned by the convolutional neural network (CNN) has led to its application in PP and DHV recognition systems. In this article, a multimodal biometric system based on PP and DHV using (VGG16, VGG19 and AlexNet) CNN models is proposed. The proposed system is uses two approaches: feature level fusion (FLF) and Score level fusion (SLF). In the first approach, the features from PP and DHV are extracted with CNN models. These extracted features are then fused using serial or parallel fusion and used to train error-correcting output codes (ECOC) with a support vector machine (SVM) for classification. In the second approach, the fusion at score level is done with sum, max, and product methods by applying two strategies: Transfer learning that uses CNN models for features extraction and classification for PP and DHV, then score level fusion. For the second strategy, features are extracted with CNN models for PP and DHV and used to train ECOC with SVM for classification, then score level fusion. The system was tested using two DHV databases and one PP database. The multimodal system is tested two times by repeating PP database for each DHV database. The system achieved very high accuracy rate.
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Benegui, Cezara, and Radu Tudor Ionescu. "Improving the Authentication with Built-In Camera Protocol Using Built-In Motion Sensors: A Deep Learning Solution." Mathematics 9, no. 15 (July 28, 2021): 1786. http://dx.doi.org/10.3390/math9151786.

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In this paper, we propose an enhanced version of the Authentication with Built-in Camera (ABC) protocol by employing a deep learning solution based on built-in motion sensors. The standard ABC protocol identifies mobile devices based on the photo-response non-uniformity (PRNU) of the camera sensor, while also considering QR-code-based meta-information. During registration, users are required to capture photos using their smartphone camera. The photos are sent to a server that computes the camera fingerprint, storing it as an authentication trait. During authentication, the user is required to take two photos that contain two QR codes presented on a screen. The presented QR code images also contain a unique probe signal, similar to a camera fingerprint, generated by the protocol. During verification, the server computes the fingerprint of the received photos and authenticates the user if (i) the probe signal is present, (ii) the metadata embedded in the QR codes is correct and (iii) the camera fingerprint is identified correctly. However, the protocol is vulnerable to forgery attacks when the attacker can compute the camera fingerprint from external photos, as shown in our preliminary work. Hence, attackers can easily remove their PRNU from the authentication photos without completely altering the probe signal, resulting in attacks that bypass the defense systems of the ABC protocol. In this context, we propose an enhancement to the ABC protocol, using motion sensor data as an additional and passive authentication layer. Smartphones can be identified through their motion sensor data, which, unlike photos, is never posted by users on social media platforms, thus being more secure than using photographs alone. To this end, we transform motion signals into embedding vectors produced by deep neural networks, applying Support Vector Machines for the smartphone identification task. Our change to the ABC protocol results in a multi-modal protocol that lowers the false acceptance rate for the attack proposed in our previous work to a percentage as low as 0.07%. In this paper, we present the attack that makes ABC vulnerable, as well as our multi-modal ABC protocol along with relevant experiments and results.
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Mei, Liu, Huaguan Li, Yunlai Zhou, Dawang Li, Wujian Long, and Feng Xing. "Output-Only Damage Detection of Shear Building Structures Using an Autoregressive Model-Enhanced Optimal Subpattern Assignment Metric." Sensors 20, no. 7 (April 6, 2020): 2050. http://dx.doi.org/10.3390/s20072050.

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This paper proposes a novel output-only structural damage indicator by incorporating the pole-based optimal subpattern assignment distance with autoregressive models to localize and relatively assess the severity of damages for sheared structures. Autoregressive models can model dynamic systems well, while their model poles can represent the state of the dynamic systems. Structural damage generally causes changes in the dynamic characteristics (especially the natural frequency, mode shapes and damping ratio) of structures. Since the poles of the autoregressive models can solve the modal parameters of the structure, the poles have a close relationship with the modal parameters so that the changes in the poles of its autoregressive model reflect structural damages. Therefore, we can identify the damage by tracking the shifts in the dynamic system poles. The optimal subpattern assignment distance, which is the performance evaluator in multi-target tracking algorithms to measure the metric between true and estimated tracks, enables the construction of damage sensitive indicator from system poles using the Hungarian algorithm. The proposed approach has been validated with a five-story shear-building using numerical simulations and experimental verifications, which are subjected to excitations of white noise, El Centro earthquake and sinusoidal wave with frequencies sweeping, respectively; the results indicate that this approach can localize and quantify structural damages effectively in an output-only and data-driven way.
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"Multi-Modal Authentication using Machine Learning Algorithm." International Journal of Engineering and Advanced Technology 8, no. 6S3 (November 22, 2019): 1151–55. http://dx.doi.org/10.35940/ijeat.f1192.0986s319.

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In the present era of information technology, there is a need to implement verification and approval strategies for security of resources. Whereas, there are number of approaches to demonstrate validation and approval, yet the biometric verification beat every other method. At first, biometrics began off with straightforward unimodal framework, the higher requirement for security had offered ascend to a prevalent framework known as multimodal verification framework. Multimodal verification confirmation has pulled in compelling interest, on account of its hugeness towards the constant application In this research proposal, an effective framework for multimodal verification authentication systems based on machine learning algorithm is employed.
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Peng, Pai, Keke Geng, Guodong Yin, Yanbo Lu, Weichao Zhuang, and Shuaipeng Liu. "Adaptive Multi-modal Fusion Instance Segmentation for CAEVs in Complex Conditions: Dataset, Framework and Verifications." Chinese Journal of Mechanical Engineering 34, no. 1 (August 30, 2021). http://dx.doi.org/10.1186/s10033-021-00602-2.

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AbstractCurrent works of environmental perception for connected autonomous electrified vehicles (CAEVs) mainly focus on the object detection task in good weather and illumination conditions, they often perform poorly in adverse scenarios and have a vague scene parsing ability. This paper aims to develop an end-to-end sharpening mixture of experts (SMoE) fusion framework to improve the robustness and accuracy of the perception systems for CAEVs in complex illumination and weather conditions. Three original contributions make our work distinctive from the existing relevant literature. The Complex KITTI dataset is introduced which consists of 7481 pairs of modified KITTI RGB images and the generated LiDAR dense depth maps, and this dataset is fine annotated in instance-level with the proposed semi-automatic annotation method. The SMoE fusion approach is devised to adaptively learn the robust kernels from complementary modalities. Comprehensive comparative experiments are implemented, and the results show that the proposed SMoE framework yield significant improvements over the other fusion techniques in adverse environmental conditions. This research proposes a SMoE fusion framework to improve the scene parsing ability of the perception systems for CAEVs in adverse conditions.
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Dissertations / Theses on the topic "Multi-modal verification systems"

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Hagemann, Andreas, and Hanna Björk. "A System for Driver Identity Verification." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4278.

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Different security issues are a top subject around the world, especially since the terror threats seem to intensify. In the same time, the transport industry suffer from problems with smuggling and theft of valuable goods. One way to increase the security might be to have a verification system installed in commercial trucks, in order to assure that the driver is the proper one.

This thesis has two purposes. One is to find appropriate methods for driver verification and build a prototype of a verification system which can be used for testing and further development. The other is to study how truck drivers perceive such a system and how their conception goes along with the growing demand for higher security. The present work is the result of a cooperation between an engineer and a cognitive scientist. The thesis focuses on the transport industry and was performed for Volvo Technology Corporation (VTEC), Gothenburg, Sweden.

Eleven available verification methods were studied. To enable a well-based selection of methods to implement in the prototype, inquiries and interviews with truck drivers and haulage contractors were carried out to complement the theoretical study.

One regular and three biometric verification methods were chosen for the test; fingerprint verification, face recognition, voice recognition and PIN verification. These methods were put together to a prototype system that was implemented in a truck simulator. A graphical user interface was developed in order to make the system user friendly. The prototype system was tested by 18 truck drivers. They were thoroughly interviewed before and after the test in order to retrieve their background, expectations and opinions as well as their perceptions and experiences of the test.

Most of the test participants were positive to the prototype system. Even though they did not feel a need for it today they believed it to “be the future”. However, some participants felt uncomfortable with the system since they felt controlled by it. It became clear how important it is to have a system that respect the users’ privacy and to assure that the users are well informed about how the system is used. Some of the technology used for the verification system requires more development to fit in the automotive context, but it is considered to be possible to achieve a secure and robust system.

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Wark, Timothy J. "Multi-modal speech processing for automatic speaker recognition." Thesis, Queensland University of Technology, 2001.

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Sanderson, Conrad, and conradsand@ieee org. "Automatic Person Verification Using Speech and Face Information." Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030422.105519.

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Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one prescribed to the card, the user is allowed access to their bank account. This scheme suffers from a major drawback: only the validity of the combination of a certain possession (the ATM card) and certain knowledge (the password) is verified. The ATM card can be lost or stolen, and the password can be compromised. Thus new verification methods have emerged, where the password has either been replaced by, or used in addition to, biometrics such as the person’s speech, face image or fingerprints. Apart from the ATM example described above, biometrics can be applied to other areas, such as telephone & internet based banking, airline reservations & check-in, as well as forensic work and law enforcement applications. Biometric systems based on face images and/or speech signals have been shown to be quite effective. However, their performance easily degrades in the presence of a mismatch between training and testing conditions. For speech based systems this is usually in the form of channel distortion and/or ambient noise; for face based systems it can be in the form of a change in the illumination direction. A system which uses more than one biometric at the same time is known as a multi-modal verification system; it is often comprised of several modality experts and a decision stage. Since a multi-modal system uses complimentary discriminative information, lower error rates can be achieved; moreover, such a system can also be more robust, since the contribution of the modality affected by environmental conditions can be decreased. This thesis makes several contributions aimed at increasing the robustness of single- and multi-modal verification systems. Some of the major contributions are listed below. The robustness of a speech based system to ambient noise is increased by using Maximum Auto-Correlation Value (MACV) features, which utilize information from the source part of the speech signal. A new facial feature extraction technique is proposed (termed DCT-mod2), which utilizes polynomial coefficients derived from 2D Discrete Cosine Transform (DCT) coefficients of spatially neighbouring blocks. The DCT-mod2 features are shown to be robust to an illumination direction change as well as being over 80 times quicker to compute than 2D Gabor wavelet derived features. The fragility of Principal Component Analysis (PCA) derived features to an illumination direction change is solved by introducing a pre-processing step utilizing the DCT-mod2 feature extraction. We show that the enhanced PCA technique retains all the positive aspects of traditional PCA (that is, robustness to compression artefacts and white Gaussian noise) while also being robust to the illumination direction change. Several new methods, for use in fusion of speech and face information under noisy conditions, are proposed; these include a weight adjustment procedure, which explicitly measures the quality of the speech signal, and a decision stage comprised of a structurally noise resistant piece-wise linear classifier, which attempts to minimize the effects of noisy conditions via structural constraints on the decision boundary.
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Sanderson, Conrad. "Automatic Person Verification Using Speech and Face Information." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/367191.

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Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one prescribed to the card, the user is allowed access to their bank account. This scheme suffers from a major drawback: only the validity of the combination of a certain possession (the ATM card) and certain knowledge (the password) is verified. The ATM card can be lost or stolen, and the password can be compromised. Thus new verification methods have emerged, where the password has either been replaced by, or used in addition to, biometrics such as the person’s speech, face image or fingerprints. Apart from the ATM example described above, biometrics can be applied to other areas, such as telephone & internet based banking, airline reservations & check-in, as well as forensic work and law enforcement applications. Biometric systems based on face images and/or speech signals have been shown to be quite effective. However, their performance easily degrades in the presence of a mismatch between training and testing conditions. For speech based systems this is usually in the form of channel distortion and/or ambient noise; for face based systems it can be in the form of a change in the illumination direction. A system which uses more than one biometric at the same time is known as a multi-modal verification system; it is often comprised of several modality experts and a decision stage. Since a multi-modal system uses complimentary discriminative information, lower error rates can be achieved; moreover, such a system can also be more robust, since the contribution of the modality affected by environmental conditions can be decreased. This thesis makes several contributions aimed at increasing the robustness of single- and multi-modal verification systems. Some of the major contributions are listed below. The robustness of a speech based system to ambient noise is increased by using Maximum Auto-Correlation Value (MACV) features, which utilize information from the source part of the speech signal. A new facial feature extraction technique is proposed (termed DCT-mod2), which utilizes polynomial coefficients derived from 2D Discrete Cosine Transform (DCT) coefficients of spatially neighbouring blocks. The DCT-mod2 features are shown to be robust to an illumination direction change as well as being over 80 times quicker to compute than 2D Gabor wavelet derived features. The fragility of Principal Component Analysis (PCA) derived features to an illumination direction change is solved by introducing a pre-processing step utilizing the DCT-mod2 feature extraction. We show that the enhanced PCA technique retains all the positive aspects of traditional PCA (that is, robustness to compression artefacts and white Gaussian noise) while also being robust to the illumination direction change. Several new methods, for use in fusion of speech and face information under noisy conditions, are proposed; these include a weight adjustment procedure, which explicitly measures the quality of the speech signal, and a decision stage comprised of a structurally noise resistant piece-wise linear classifier, which attempts to minimize the effects of noisy conditions via structural constraints on the decision boundary.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Microelectronic Engineering
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Book chapters on the topic "Multi-modal verification systems"

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Heider, Thomas, and Thomas Kirste. "Architecture Considerations for Interoperable Multi-modal Assistant Systems." In Interactive Systems:Design, Specification, and Verification, 253–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36235-5_19.

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Polgreen, Elizabeth, Kevin Cheang, Pranav Gaddamadugu, Adwait Godbole, Kevin Laeufer, Shaokai Lin, Yatin A. Manerkar, Federico Mora, and Sanjit A. Seshia. "UCLID5: Multi-modal Formal Modeling, Verification, and Synthesis." In Computer Aided Verification, 538–51. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13185-1_27.

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AbstractUCLID5 is a tool for the multi-modal formal modeling, verification, and synthesis of systems. It enables one to tackle verification problems for heterogeneous systems such as combinations of hardware and software, or those that have multiple, varied specifications, or systems that require hybrid modes of modeling. A novel aspect of UCLID5 is an emphasis on the use of syntax-guided and inductive synthesis to automate steps in modeling and verification. This tool paper presents new developments in the UCLID5 tool including new language features, integration with new techniques for syntax-guided synthesis and satisfiability solving, support for hyperproperties and combinations of axiomatic and operational modeling, demonstrations on new problem classes, and a robust implementation.
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Wu, Huilin, and Xiaocui Zhang. "Software Trustworthy Modeling and Verification Algorithm for CPS Time Attributes." In Application of Intelligent Systems in Multi-modal Information Analytics, 448–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74814-2_63.

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Xie, Jiahui, Sihao Zhao, Xiaolan Nie, Chu Lin, and Jun Ye. "Query and Verification of Encrypted Data Based on Blockchain in Multi-user Scenario." In Application of Intelligent Systems in Multi-modal Information Analytics, 702–7. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74811-1_101.

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Cuzzocrea, Alfredo, Majid Abbasi Sisara, and Carmine Gallo. "Experimental Analysis and Verification of a Multi-modal-Biometrics Identity Verification Framework Based on the Dempster-Shafer Theory." In Complex, Intelligent and Software Intensive Systems, 118–29. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08812-4_12.

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Verlinde, P., and M. Acheroy. "On the Use of Statistical Considerations in Multi-Modal Identity Verification System Design." In Multisensor Fusion, 807–14. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_40.

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Hao, Bin, and Xiali Hei. "Voice Liveness Detection for Medical Devices." In Advances in Medical Technologies and Clinical Practice, 109–36. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7525-2.ch005.

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Many healthcare providers integrate biometric recognition/verification schemes into patient identification or other information security systems. While overcoming the disadvantages of using passwords, PINs, and tokens which may be forgotten, or stolen, biometric systems are susceptible to spoofing attacks, or presentation attacks. Liveness detection is an effective mechanism used to defeat a presentation attack. This chapter focuses on voice liveness detection in automatic speaker verification (ASV) systems. The authors explain the spoofing attacks to ASV systems comprising impersonation, voice conversion, speech synthesis, and replay and then present four types of liveness detection (anti-spoofing) methods used to mitigate ASV spoofing attacks: challenge-response-based methods, acoustic feature-based methods, hardware-based methods, and multi-modal biometric-based methods. This chapter analyzes the advantages and disadvantages of each kind of liveness detection method and proposes the possible application of voiceprint-based liveness detection schemes in the insulin pump system.
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Tsatsoulis, P. Daphne, Aaron Jaech, Robert Batie, and Marios Savvides. "Multimodal Biometric Hand-Off for Robust Unobtrusive Continuous Biometric Authentication." In IT Policy and Ethics, 389–409. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2919-6.ch018.

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Conventional access control solutions rely on a single authentication to verify a user’s identity but do nothing to ensure the authenticated user is indeed the same person using the system afterwards. Without continuous monitoring, unauthorized individuals have an opportunity to “hijack” or “tailgate” the original user’s session. Continuous authentication attempts to remedy this security loophole. Biometrics is an attractive solution for continuous authentication as it is unobtrusive yet still highly accurate. This allows the authorized user to continue about his routine but quickly detects and blocks intruders. This chapter outlines the components of a multi-biometric based continuous authentication system. Our application employs a biometric hand-off strategy where in the first authentication step a strong biometric robustly identifies the user and then hands control to a less computationally intensive face recognition and tracking system that continuously monitors the presence of the user. Using multiple biometrics allows the system to benefit from the strengths of each modality. Since face verification accuracy degrades as more time elapses between the training stage and operation time, our proposed hand-off strategy permits continuous robust face verification with relatively simple and computationally efficient classifiers. We provide a detailed evaluation of verification performance using different pattern classification algorithms and show that the final multi-modal biometric hand-off scheme yields high verification performance.
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Conference papers on the topic "Multi-modal verification systems"

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Changhan Park, Joonki Paik, Taewoong Choi, Soonhyob Kim, Youngouk Kim, and J. Namkung. "Multi-Modal Human Verification Using Face and Speech." In Fourth IEEE International Conference on Computer Vision Systems (ICVS'06). IEEE, 2006. http://dx.doi.org/10.1109/icvs.2006.45.

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Biggio, Battista, Zahid Akthar, Giorgio Fumera, Gian Luca Marcialis, and Fabio Roli. "Robustness of multi-modal biometric verification systems under realistic spoofing attacks." In 2011 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2011. http://dx.doi.org/10.1109/ijcb.2011.6117474.

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Vidal-Calleja, Teresa, Jaime Valls Miro, Fernando Martin, Daniel C. Lingnau, and David E. Russell. "Automatic detection and verification of pipeline construction features with multi-modal data." In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE, 2014. http://dx.doi.org/10.1109/iros.2014.6942993.

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Oussaid, Linda Mohand, Yamine Ait Ameur, and Mohamed Ahmed Nacer. "A generic formal model for fission of modalities in output multi–modal interactive systems." In Third International Workshop on Verification and Evaluation of Computer and Communication Systems (VECoS 2009). BCS Learning & Development, 2009. http://dx.doi.org/10.14236/ewic/vecos2009.12.

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Cuzzocrea, Alfredo, and Enzo Mumolo. "Dempster-Shafer-Based Fusion of Multi-Modal Biometrics for Supporting Identity Verification Effectively and Efficiently." In 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS). IEEE, 2021. http://dx.doi.org/10.1109/ichms53169.2021.9582651.

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Zhang, Fengling, Lin Li, Yu Fan, and Jiuzhou Liu. "Numerical Analysis and Experimental Verification of Synchronized Switching Damping Systems." In ASME 2019 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/smasis2019-5570.

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Abstract Synchronized switch damping (SSD) technique utilizing piezoelectric elements is one of good methods of vibration control. This work develops numerical tools for dynamic analysis of structures with SSD dampers, and conducts experiments to verify the effectiveness. A cantilevered beam bonded with piezoelectric patches is considered as the host structure. Two types of SSD circuits are considered, one with no subsequent electric element (SSDS) and another with inductance (SSDI). Firstly, a lumped parameter electromechanical coupling model is employed, with parameters determined experimentally. Then, the frequency response curves of the nonlinear vibration systems are analyzed by the multi-harmonic balance method combined with alternating frequency-time techniques (MHBM/AFT). In order to verify the proposed method, an experimental study is performed. In the experiment SSD circuit is realized by an enhanced analog circuit which is more complex but also more stable than the original SSD circuits. The measured results are compared with those obtained by proposed numerical tools with good agreements. It is also shown that the modal frequencies and modal shapes of SSD systems are almost unchanged with the vibration amplitudes, which indicates that the nonlinear force generated by SSD has little influence on the characteristics of linear structure. It is verified both numerically and experimentally that SSDI damper can produce significant damping for multiple modes.
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7

Zhang, Chaofan, Daogang Lu, Yu Liu, and Haotian Qiu. "Modal analysis and additional fluid mass of coaxial three-layer thin-wall member in annular flow gap." In 2022 29th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icone29-92274.

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Abstract The sodium-cold pool-type fast reactor is the preferred reactor type for the fourth generation advanced reactor research. The reactor structure of a pool-type fast reactor has the characteristics of large size, thin wall, and relatively low stiffness. Therefore, the seismic design, analysis, and verification of the pool-type fast reactor are the key concerns of the fast reactor safety evaluation. In the fast reactor, many important reactor structures, such as the main pump support cylinder, are immersed in the ring basin of the reactor body. These devices are equipped with thermal shields and can therefore be regarded as coaxial housing systems with water gaps between the cylinders. Under the seismic condition, the fluid-structure coupling effect between the shell and the water gap will change the natural frequency of the shell itself, and the change of structural vibration characteristics is very important to study the seismic performance of the equipment structure in the fast reactor. However, the calculation method based on the dry/wet modal frequency ratio is not suitable for the calculation of additional mass under non-full water conditions. In this paper, based on ANSYS finite element analysis method, a coaxial three-layer shell model with fluid clearance is established to simulate the main pump supporting cylinder and other equipment, and the modes under different frequencies are studied. The additional mass of fluid clearance under different modes is calculated by the relationship between fluid pressure field and shell areal-displacement during vibration. The results are of reference value to the structural design and test of coaxial multi-layer shells such as the support cylinder of the main pump of the fast reactor.
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8

Li, Hong-Nan, Ying Jia, and Su-Yan Wang. "Experimental Verifications on Seismic Response Control of Tall Structures by Multiple Tuned Liquid Dampers." In ASME/JSME 2004 Pressure Vessels and Piping Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/pvp2004-2902.

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The focus of this paper is on the use of multiple Tuned Liquid Dampers (TLDs) as passive control devices to reduce the multi-modal responses of tall buildings and high-rise structures to earthquake ground motion excitation. A model of a 3-story building with one and two TLDs was installed on a shaking-table. The system was subjected to three earthquake time histories. Then the mechanical models and the equations of motion for the systems of tall buildings and high-rise structures with TLDs are established. Here, the solution of the dynamic liquid pressure is based on the method of the Volume of Fluid and the seismic responses are obtained by use of the state equation. The comparisons show that theoretical results are generally in good agreement with experiments. It is observed that the approach presented in this paper proved to be quite effective both in the numerical example and in the seismic simulating tests.
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