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Biometric authentication using machine learning

Windows 10 puts biometric security front and center

Biometric signature authentication using machine learning

Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities. Kiran Bibi 1, Saeeda Naz 1 & Arshia Rehman 1 Multimedia Tools and Applications volume 79, pages 289-340 (2020)Cite this articl HEARTWAVE BIOMETRIC AUTHENTICATION USING MACHINE LEARNING ALGORITHMS Chin Leng Peter, LIM A thesis submitted to the Newcastle University for the degree of Doctor of Philosophy School of Engineering Faculty of Science, Agriculture and Engineering December 201 DOI: 10.1007/s11042-019-08022- Corpus ID: 199576552. Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities @article{Bibi2019BiometricSA, title={Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities}, author={Kiran Bibi and Saeeda Naz and A. Rehman}, journal={Multimedia. One way is through biometric authentication which uses physiological and behavioral measures to authenticate users before granting them access to services or systems. This paper presents the various biometric technologies that are based on machine learning techniques

Biometrics aims at reliable and robust identication of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter ap- plications. Frequently considered modalities are ngerprint, face, iris, palmprint and voice, but ther This paper is targeted in the area of biometric data enabled security by using machine learning for the digital health. The traditional authentication systems are vulnerable to the risks of forgetfulness, loss, and theft. Biometric authentication is has been improved and become the part of daily life. The Electrocardiogram (ECG) based authentication method has been introduced as a biometric.

A Machine Learning Framework for Biometric Authentication Using Electrocardiogram Abstract: This paper introduces a framework for how to appropriately adopt and adjust machine learning (ML) techniques used to construct electrocardiogram (ECG)-based biometric authentication schemes Benefit #2 - Machine Learning Expedites the Process - When machine learning is used within biometrics systems, it's able to detect patterns in behavior and intent. This can speed up the process, providing an improved experience for consumers. Benefit #3 - Machine Learning Simplifies Data Sets - When you add machine learning to a. Machine learning approaches are used today to fingerprint recognition. To design a classifier, training first based on the data collected and then test the classifier by using the same training data or other data. Early on, many researches try to accurately identify the fingerprint, for example two machine learning approaches Biometric Authentication System A biometric authentication system is a real-time system to verify a person's identity by measuring particular characteristics or behavior of the person's body. Biometric devices such as iris scanners collect a person's biometric data and transform them into digital forms The field itself got whole new name — Machine Learning. My current shot to crack biometric authentication is based on Keras library which itself is based on TensorFlow library open sourced by Google. Front-end screen is based on -flow boilerplate by Max Stoiber based on React

Biometric Authentication Using Machine Learning Techniques

Biometrics and machine learning: the accurate, secure way to access your bank. Biometric advances are already being used in a multitude of financial sector processes. Artificial intelligence can analyse speech and facial characteristics to create a digital identity that allows for a much more secure online setting, where there's a. This paper introduces a framework for how to appropriately adopt and adjust Machine Learning (ML) techniques used to construct Electrocardiogram (ECG) based biometric authentication schemes. The proposed framework can help investigators and developers on ECG based biometric authentication mechanisms define the boundaries of required datasets and get training data with good quality. To. Machine learning also studies the biometric topographies to simulate an individual's identification learning activities. Machine learning has made the functioning of biometrics identification possible and has also made much advancement in biometric pattern recognition Fingerprint Identification for Biometric Authentication Fingerprint identification is considered the oldest and most developed biometric authentication method. There are different machine learning algorithms that you can use for fingerprint identification. The most common is based on the selection of details

Meanwhile, behavioral biometrics is governed by a dynamic approach driven by machine learning and deep learning, which involves gathering and processing very large data sets. Using primary device sensors such as accelerometer, gyroscope, and touch, hundreds, possibly thousands of behavioral patterns can be used to authenticate users continuously A biometric system for identification or recognition can be designed and implemented either feature-based using a handcrafted feature extraction or automatic feature generation-based using an end-to-end training based on a machine learning algorithm This book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve problems associated with biometric security such as authentication, indexing, template protection, spoofing attack detection, ROI detection, gender classification etc. This text highlights a showcase of cutting-edge research on the use of convolution neural networks. This paper introduces a framework for how to appropriately adopt and adjust machine learning (ML) techniques used to construct electrocardiogram (ECG)-based biometric authentication schemes. The proposed framework can help investigators and developers on ECG-based biometric authentication mechanisms define the boundaries of required datasets and get training data with good quality The application of behavioral-based biometric authentication basically contains three major modules, namely, data capture, feature extraction, and classifier. This application is focusing on extracting the behavioral features related to the user and using these features for authentication measure

An Enhanced Machine Learning-Based Biometric

  1. For More Details Contact Name:Venkatarao GanipisettyMobile:+91 9966499110Email :venkatjavaprojects@gmail.comWebsite:www.venkatjavaprojects.comAbout Project:I..
  2. E-commerce is the sector that is benefitting the most from the silent authentication using behavioural biometrics and machine learning for improving the user experience and fostering long-term customer relations. This authentication method can identify consumers in real-time, which helps retailers in catering to changing needs
  3. ation conditions to achieve uniform.

The different machine learning and data mining methods used by authentication and authorization schemes for mobile IoT devices are provided. Threat models and countermeasures used by biometrics-based authentication schemes for mobile IoT devices are also presented The biometric authentication flow includes facial capture and voice recognition. An off-the-shelf WebRTC service was implemented as there was a need to process media data to the server. Machine Learning for Biometric Recognition The product has to be able to identify the user based on voice, photo, and questions The investments organizations have already made in cybersecurity can be put to better use, Oliveira contends, taking action driven by machine learning and subject matter rules based on technology that brings together biometrics, behavioral analysis, and the other information already coming from security systems authentication configurations, and for 15 out of 16, makes it lower than FPR. For reproducibility, we have made our codebase public.3 We note that a key difference in the use of machine learning in biometric authentication as compared to its use in other areas (e.g., predicting likelihood of diseases through a healthcar Machine learning-based mobile biometric authentication. Figure 4A schematically describes the process of AI-based biometric authentication for speaker identification using a mobile sensor module. The integrated PMAS module consisted of a mini PMAS, signal transmitter, and machine learning processor

The biometric data can provide clinicians and researchers with actionable insights that can be used for example, for both therapeutic and preventive care. Improvements in data analysis and the availability of cloud-based machine learning tools makes data analysis possible VOLUME XX, 2017 1 [Notice] This paper has been published in the IEEE Access and the proper citation is as follows: Song-Kyoo Kim, Chan Yeob Yeun, Ernesto Damiani and Nai-Wei Lo, A Machine Learning Framework for Biometric Authentication using Electrocardiogram, IEEE Access 7 (2019), pp. 94858-94868 A breakthrough approach to improving biometrics performance Constructing robust information processing systems for face and voice recognition Supporting high-performance data fusion in multimodal systems Algorithms, implementation techniques, and application examples - Selection from Biometric Authentication: A Machine Learning Approach [Book A Machine Learning Framework for Biometric Authentication using Electrocardiogram. 03/29/2019 ∙ by Song-Kyoo Kim, et al. ∙ 0 ∙ share . This paper introduces a framework for how to appropriately adopt and adjust Machine Learning (ML) techniques used to construct Electrocardiogram (ECG) based biometric authentication schemes

This paper is targeted in the area of biometric data enabled security system based on the machine learning for the digital health. The disadvantages of traditional authentication systems include the risks of forgetfulness, loss, and theft. Biometric authentication is therefore rapidly replacing traditional authentication methods and is becoming an everyday part of life This paper introduces a framework for how to appropriately adopt and adjust Machine Learning (ML) techniques used to construct Electrocardiogram (ECG) based biometric authentication schemes. The proposed framework can help investigators and developers on ECG based biometric authentication mechanisms define the boundaries of required datasets and get training data with good quality

Machine learning: driving significant improvements in biometric performance. As they improve, biometric authentication systems are becoming increasingly indispensable for protecting life and property. This book introduces powerful machine learning techniques that significantly improve biometric performance in a broad spectrum of application. In closing, Biometric Authentication: A Machine Learning Approach is intended for one-semester graduate school courses in machine learning, neural networks, and biometrics. It is also intended for professional engineers, scientists, and system integrators who want to learn systematic, practical ways of implementing computationally intelligent. Smart cars can be made better and safer through driver authentication powered by biometrics technology, according to a recent study that proposed a new technique that captures and identifies a driver's eye movements with a novel machine-learning algorithm. You've reached the limit of three articles per month for unregistered users Heartwave biometric authentication using machine learning algorithms . By Chin Leng Peter Lim. Get PDF (6 MB) Abstract. PhD ThesisThe advancement of IoT, cloud services and technologies have prompted heighten IT access security. Heartwave as biometric mode offers the potential due to the inability to falsify the signal and ease of signal. Keywords-Keystroke dynamics, authentication, identification, biometrics, classification, machine learning; I. INTRODUCTION Computers have become a staple in our everyday lives. We heavily depend on computers to store and process sensitive information. Intruders are everywhere, and capable of attacking an individual's or large organization.

A Machine Learning Framework for Biometric Authentication

  1. Machine Learning and Biometric Systems. Machine learning is the systematic study of scientific algorithms that provide the system with the ability to simulate human learning activities without being explicitly programmed. Machine learning also studies the biometric topographies to simulate an individual's identification learning activities
  2. Active Authentication Using Behavioral Biometrics and Machine Learning A Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at George Mason University By Ala'a Arif El Masri Master of Science University of North Carolina at Charlotte, 2006 Bachelor of Science Coastal Carolina University, 200
  3. An Enhanced Electrocardiogram Biometric Authentication System Using Machine Learning EBRAHIM AL ALKEEM1, SONG-KYOO KIM 2, CHAN YEOB YEUN 1,2, MOHAMED JAMAL ZEMERLY1, KIN FAI POON3, GABRIELE GIANINI3,4, AND PAUL D. YOO 5,6 1Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi 127788, UA

A Machine Learning Framework for Biometric Authentication using Electrocardiogram. Click To Get Model/Code. This paper introduces a framework for how to appropriately adopt and adjust Machine Learning (ML) techniques used to construct Electrocardiogram (ECG) based biometric authentication schemes. The proposed framework can help investigators and developers on ECG based biometric. Proposed a Machine Learning-based authentication framework for Smart IoT Devices. • The proposed biometric authentication system is based on cloud platform. • The privacy issues underpinning the use of biometrics for authentication are well addressed We present a deep learning approach [12], which is a very powerful advanced machine leaning method, to the challenging problem of keystroke dynamics biometric. We further take advantage of the rich sensor modalities available for mobile devices and strengthen our keystroke dynamics biometrics using multi-modal typing features Using machine learning (ML) is very popular field of research nowadays not only for biometrics but also other fields of researches. When biometric systems are evaluated, there have been many examples, applications, techniques, approaches or methods to perform biometrics recognition using ML

Machine learning masters the fingerprint to fool biometric systems Fingerprint authentication systems are a widely trusted, ubiquitous form of biometric authentication, deployed on billions of. The science behind voice biometrics is not new, but effective business implementations in the area of customer authentication are made possible by advances in machine learning and computational power. Interactions continuously improves technologies including our voice biometrics authentication using machine learning and deep neural networks Disadvantages of biometric authentication. Despite increased security, efficiency, and convenience, biometric authentication and its uses in modern-day tech and digital applications also has disadvantages: Costs - Significant investment needed in biometrics for security. Data breaches - Biometric databases can still be hacked Based on various application scenarios on ECG based authentication, three distinct use cases (or authentication categories) are developed. With more qualified training data given to corresponding machine learning schemes, the precision on ML-based ECG biometric authentication mechanisms is increased in consequence

How Machine Learning and Biometric Technology Work Togethe

Biometric-based authentication is promising for IoT due to its convenient nature and lower susceptibility to attacks. Additionally, machine learning and deep learning techniques are delivering a promising solution to biometric systems and to increase the accuracy and plays a decisive role for presentation attack detection The authentication is based on keystroke dynamics which captures the user's behavioral biometric and applies machine learning concepts to classify them. The users type a strong passcode .tie5Roanl to record their typing pattern Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms. 03/10/2021 ∙ by Arafat Rahman, et al. ∙ 0 ∙ share . With the rapid advancement of technology, different biometric user authentication, and identification systems are emerging Introduction. Now days, Biometrics is a widely established measure in security and authentication processes in several system applications. According to International Standard Organization (ISO), biometric is defined as a means of biological process for recognizing and analyzing an individual based on their physiological (fingerprint, face, iris, palm prints) and behavioral characteristics. Biometric authentication is replacing typical identification and access control systems to become a part of everyday life [1], [2]. The electrocardiogram (ECG) is one of the most recent traits to be explored for biometric purposes [3], [4]. ECGs report electrical conduction through the heart and can be used to recognize specific individuals [5]

Behavioral biometrics allow us to create beautiful and elegant user experiences that take what is unique about a user to uniquely identify and authenticate them without them having to do anything different. They can simply go about their day and the machine learning models can do their magic in the background. Some of the use cases where we can. Machine learning security needs new perspectives and incentives. This face map will later be used by the customer to access his or her account using biometric authentication. The company now knows that they have taken the necessary steps to prove the potential customer's identity. They can now complete the required background checks to. The latter section delves into wearable sensors, the quality of behavioral biometric data, and biometric fusion. Using biometrics with mobile devices could potentially help make authentication faster and easier, but there are challenges with mobile device biometrics in general and also specifically for first responders, the draft notes Continuous User Authentication on Touchscreen Using Behavioral Biometrics Utilizing Machine Learning Approaches: 10.4018/978-1-7998-2701-6.ch013: Nowadays, touchscreen mobile devices make up a larger share in the market, necessitating effective and robust methods to continuously authenticate touch-base

Using Deep Learning for finger-vein based biometric

Biometric Authentication using typing pattern by Rahul

  1. as first name, last name, address, phone, e-mail or pass- • It was seen that; machine learning techniques are used words, in some cases, even credit card information etc. not only biometric recognition-authentication process, but Individuals might also get involved crimes unintentionaly also to provide secure platform for biometric.
  2. Self-powered hybrid sensor-driven keystroke dynamics-based biometric authentication using a neural network. a) Procedure of proposed keystroke dynamics-based user identification and authentication system using a neural network. Most of the state-of-the-art works used support vector based machine learning (SVM) instead of a deep neural.
  3. Machine Learning Masters the Fingerprint BROOKLYN, New York, Tuesday, November 20, 2018 - Fingerprint authentication systems are a widely trusted, ubiquitous form of biometric authentication, deployed on billions of smartphones and other devices worldwide
  4. In fact, some banks are deploying voiceprint, an ML-driven biometric solution, to authenticate customers using only their voices. Solutions like HPE's Deep Learning have emerged to help companies accelerate data analysis for reliable, real-time results. Machine Learning Can Help Banks Root Out Frau

User Security Evolved. Authenticate users based on what they do, not what they know. UnifyID offers Multi-Factor Authentication services that combine deterministic, behavioral biometric and environmental attributes using machine learning to uniquely identify users. And all the user has to do is be themselves Plurilock provides invisible MFA and continuous authentication solutions using state-of-the-art behavioral-biometric and machine learning technology. Plurilock enables organizations to compute safely—and with peace of mind Biometric authentication is designed to protect systems from external threats. Password, knowledge, token, and out of band based authentication are the things of the past. Biometric authentication technology protects against frauds as it depends on biometric data unique to every individual customer

Biometrics and machine learning: the accurate, secure way

Taking telemetry data from the mobile device and using Machine Learning and Artificial Intelligence, to learn and match user behaviour, InMotion complements the biometric check of technologies such as TouchID or FaceID, to bring heightened levels of security to organisations who are looking for extra security for user access Plurilock leverages state-of-the-art behavioral-biometric, environmental, and contextual technologies to provide invisible, adaptive, and risk-based authentication solutions with the lowest possible cost and complexity. In this 30 minute webinar learn how Plurilock bring behavioral-biometric authentication driven by machine learning to your Gluu deployment COVID-19, Biometric Authentication, and the Low-Touch Economy. Facial recognition may be the hottest form of biometric authentication. But it's far from the only - or even the most effective - biometric authentication method for all instances. In fact, as far as Redrock Biometrics is concerned, a superior alternative may lie in the palm.

Machine Learning and Biometrics System - Javatpoin

  1. How Enterprises are Using Voice Biometrics for Authentication. Voice authentication offers a flexible and cost-effective form of biometric authentication as it does not require hardware integration that might be needed in the case of other modalities such as fingerprint matching or retinal scan
  2. Behavioral biometrics are an incredibly powerful component of a frictionless authentication ecosystem. Play. smart. Unparalleled intelligence. Coupling behavioral biometrics with Callsign's Intelligence Engine lets you stay ahead of threats and manage risks. Actively. alert. 3FA from 1 interaction
  3. The Advantages of Biometric Authentication. Biometric authentication enables online businesses to reliably authenticate users for regular s, high-risk transactions and for a variety of emerging use cases. And most importantly, it helps nullify the risk of ATO since it does not rely on a username and password, which could have easily been.
  4. Using mobile for example behavioral biometric authentication (BBA) will use mobile sensor tech like Using a combination of machine learning and analytics to enable real-time cognitive.
  5. Biometric authentication is defined as a security measure that matches the biometric features of a user looking to access a device or a system. Access to the system is granted only when the parameters match those stored in the database for that particular user. Click here to learn about the basics of biometric authentication and the top seven.
  6. g increasingly indispensable for protecting life and property. This book introduces powerful machine learning techniques that significantly improve biometric performance in a broad spectrum of application domains
Future Technology Vision Concept, Communication People

Before understanding the motivation of deep learning in biometrics we must first understand the motivation of machine learning in biometrics. Biometric recognition is not the only domain of security that machine learning has been instrumental in; it has immense application in all kinds of security domains, for example, vast automation in. Like other biometric using face, iris, and finger, the ear as a biometric contains a large amount of specific and unique features that allow for human identification. The ear morphology changes slightly after the age of 10 years and medical studies have shown that significant changes in the shape of the ear happen only before the age of 8 years and after the age of 70 years

all kind of systems and applications. This work investigates the use of an ECG signal in biometrics systems approaching machine learning techniques. This signal is a new alternative not only to increase current safety standards by providing the individual's continuous authentication but also to assess health with cardiac monitoring already wel Source. Applying machine learning techniques to biometric security solutions is one of the emerging AI trends.Today I would like to share some ideas about how to develop a face recognition-based biometric identification system using OpenCV library, DLib and real-time streaming via video camera Biometric Authentication. Biometric authentication's aim is to verify that you are who you are supposed to be. With such systems, a computer will scan a person for inherent attributes - for instance, a face recognition template, and will then compare the individual's characteristics to a template stored within a database Facial recognition is a biometric technology that uses uniquely distinguishable facial features to identify a person. Consequently, a facial recognition solution uses biometrics to map facial features from a photograph or video and compares the information with a database of known faces to find a match and the pervasiveness of handheld computation devices, biometric user authentication (and identification) is rapidly becoming ubiq-uitous. Modern approaches to biometric authentication, based on sophisticated machine learning techniques, cannot avoid storing either trained-classifier details or explicit user biometric data

Multimodal Biometric Verification for Business Security

Biometrics is the science and technology of authentication by identifying the living individual's physiological or behavioral attributes. Keystroke dynamics is a behavioral measurement and it utilizes the manner and rhythm in which each individual types Fingerprint recognition engine for Java that takes a pair of human fingerprint images and returns their similarity score. Supports efficient 1:N search. java fingerprint fingerprint-authentication feature-extraction biometrics minutia fingerprint-recognition sourceafis. Updated on Apr 2 AI-based typing biometrics might be authentication's next big thing Advances in machine learning pave the way for typing-based authentication services like TypingDNA biometric database consisting of radio biometrics of seven people collected over a period of two months. We leverage this database to create machine learning (ML) models that make the proposed system adaptive to new in-car environments. Secondly, we study the performance of the in-car driver authentication system with increasing effective.

This thesis focuses on the effective classification of the behavior of users accessing computing devices to authenticate them. The authentication is based on keystroke dynamics, which captures the users behavioral biometric and applies machine learning concepts to classify them. The users type a strong passcode .tie5Roanl to record their typing pattern. In order to confirm identity. A new authentication approach could improve the security of current biometric techniques that rely on video or images of users' faces. Known as Real-Time Captcha, the technique uses a unique.

cremental learning has been successfully integrated into various machine learning algo-rithms such as decision trees, neural networks, and support vector machines (SVMs) [24. 28]. We propose a method for ECG-based biometric authentication with incremental learning of features. The method provides continuous training using new ECG signals a Howard's focuses on providing insights on one simple question: How can behavioral biometrics combined with machine learning and risk assessment techniques provide a much more innovative approach to online user authentication? But before we tackle this question, let's quickly discuss what Behavioral Biometrics are python data-science machine-learning privacy biometric-authentication novelty-detection uses Jetpack Security to encrypt user data such as text files or images using Encrypted Shared Preferences and uses Biometrics for authentication. The data can only be decrypted after the user is authorized via a master key or Biometric authentication. Why do we need biometric authentication? The use of a biometric identification system creates a strong connection between a person and a data record, unlike the use of other types of authentication factors, such as passwords or tokens. Yes. Biometrics ranked fifth on the emerging technologies (machine learning) list for 2020. Conclusion

Machine learning and biometrics: How AI is becoming more

Biometric systems had started using cutting edge machine learning and big data technologies to improve security as well as the system performance. User Behavior Analytics (UBA), an approach based on user behavior uses big data and advanced algorithms to assess user risk The first biometric authentication method to appear on mainstream smartphones, capacitive fingerprint scanning is fast and provides low FARs. Unlike early optical scanners, which would essentially take a photo of a user's fingerprint, capacitive scanners detect the ridges of your fingerprint as it touches a conductive plate EEG-Based Biometric Authentication Using Gamma Band Power During Rest State. Circuits, Systems, and Signal Processing (2017): 1-13.> In 2017, Zhendong presented a high performance EEG biometric achieved by extracting EEG signals features using four types of entropies Password guessing using machine learning technology is a new threat that's difficult to withstand. This type of attack requires innovative methods of protection and quick response. Apriorit has more than 10 years of expertise in cybersecurity and encryption techniques, and we would be glad to assist you in protecting your web application. FaceAuthMe TM uses sophisticated machine learning and AI algorithms that capture intelligently facial biometrics and other data of the customer to uniquely identify a genuine customer from a fraudster. FaceAuthMe TM provides a one-stop, seamless authentication experience for all authentication needs. The novelty and innovative edg

Video: Introductory Chapter: Machine Learning and Biometrics

Biometrics can be the authentication silver bullet as it combines security and a convenient UX, with leading fingerprint sensors authenticating in under a second. Its capacity to bring security to devices and processes previously either unsecured, poorly secured, or secured with a poor UX is phenomenal This will create a secure and user-friendly authentication method. Advances in artificial intelligence and machine learning have opened up a new identification method that analyzes how users interact with a device they use as an authentication tool. User behavior can be applied to identify someone, and it does require storing large amounts of data By combining our expertise in passive authentication, behavioral biometrics, and machine learning with Prove's established mobile identity solutions, Prove will become the most complete mobile identity solution in the market, he stated In Proceedings of the ACM Conference on Computer and Communications Security (New York, NY, USA, oct 2017), Association for Computing Machinery, pp. 135--147. Google Scholar Digital Library. Mondal, S., and Bours, P. A study on continuous authentication using a combination of keystroke and mouse biometrics Biometric Authentication: A Machine Learning Approach When the book tries to make the leap to connecting the machine learning techniques to biometric authentication in a meaningful way such that a computer scientist could code up an algorithm, the book really falls on its face. There are some nice block diagrams of biometric systems, but no.

Purchase and transactions: Biometric authentication is also being applied to the payment space, wherein companies such Paypal, Visa, Apple, Google, and Samsung are using biometric recognition as part of multi-factor authentication to purchase. Using (or learning) an individual's unique fingerprint to authenticate payment can increase security. By combining our expertise in passive authentication, behavioral biometrics, and machine learning with Prove's established mobile identity solutions, Prove will become the most complete mobile. Identification of User Behavioral Biometrics for Authentication using Keystroke Dynamics and Machine Learning. By Sowndarya Krishnamoorthy A Thesis Submitted to the Faculty of Graduate Studies through the School of Computer Science in Partial Fulfillment of the Requirements for the Degree of Master of Science at the University of Windso

Biometric Based Personal Authentication using Eye Movement Tracking. Atul Dhingra. Bijaya Panigrahi. M. Hanmandlu. Amioy Kumar. Atul Dhingra. Bijaya Panigrahi. M. Hanmandlu. Amioy Kumar. Related Papers. 3sci.1-s2.-S0921889014002632-main-min (1).pdf. By Manish Raj. Biometric gait identification based on a multilayer perceptron TUBA is a remote biometric authentication system based on keystroke-dynamics information. We use machine-learning techniques to detect intruders merely based on keystroke dynamics, i.e., timing information of keyboard events. We allow for certain types of key event injection by bots. 2.1. Security assumptions and malware attack mode U.S. Patent No. 9,559,852 is a continuation of mSIGNIA's earlier Digital Biometric patents that use machine learning to identify users and recognize devices in a consumer friendly and privacy compliant manner. During the process in which a user configures and personalizes their laptop, tablet or smartphone, data is added to the device