Optimizing digraph-latency based biometric typist verification systems: inter and intra typist differences in digraph latency distributions

  title={Optimizing digraph-latency based biometric typist verification systems: inter and intra typist differences in digraph latency distributions},
  author={Doug Mahar and Renee Napier and Michael Wagner and William Laverty and Ron Henderson and Michael Hiron},
  journal={Int. J. Hum. Comput. Stud.},
Umphress and Williams have shown that individual differences in digraph latency may provide a means of accurately verifying the identity of computer users. The present research refined this technique by exploring inter and intra subject differences in digraph latency distributions. Experiment 1 showed that there is marked heterogeneity in the latency with which individual subjects type different digraphs. Consequently, it was found that typist verification accuracy improved when a digraph… 
A Long-term Trial of Keystroke Profiling using Digraph, Trigraph and Keyword Latencies
The results demonstrate that the techniques offer significant promise as a means of non-intrusive identity verification during keyboard-related activities, with an optimum false acceptance rate of 4.9% being observed at a rate of 0% false rejection.
Gaussian Mixture Modeling of Keystroke Patterns for Biometric Applications
A novel up--up keystroke latency (UUKL) feature is proposed and its performance with existing features is compared using a Gaussian mixture model (GMM)-based verification system that utilizes an adaptive and user-specific threshold based on the leave-one-out method (LOOM).
Biometric Authentication and Identification using Keystroke Dynamics: A Survey
The use and acceptance of this biometric could be increased by development of standardized databases, assignment of nomenclature for features, development of common data interchange formats, establishment of protocols for evaluating methods, and resolution of privacy issues.
User authentication through keystroke dynamics
This paper presents an original measure for keystroke dynamics that limits the instability of this biometric feature, and has tested this approach on 154 individuals.
Evaluation on a keystroke authentication system by keying force incorporated with temporal characteristics of keystroke dynamics
Developing and evaluating techniques to authenticate valid users, using the keystroke dynamics of a user's PIN number entry on a numerical keypad, with force sensing resistors concluded that it was difficult to obtain enough information to behave as a perfect impostor by monitoring the videotaped keystrokes.
Application of Recurrent Neural Networks for User Verification based on Keystroke Dynamics
The history of biometrics and the state of the art in keystroke dynamics is introduced and an algorithm for human verification based on these data is presented, a classifier based on recurrent neural networks (LSTM and GRU).
A machine learning approach to keystroke dynamics based user authentication
The results from this study indicate that the Equal Error Rate (EER) is significantly influenced by the attribute selection process and to a lesser extent on the authentication algorithm employed, and provides evidence that a Probabilistic Neural Network (PNN) can be superior in terms of reduced training time and classification accuracy when compared with a typical MLFN back-propagation trained neural network.
Typing Biometrics: Impact of Human Learning on Performance Quality
This study investigates the validity of the assumption that typing biometric patterns are stable over time by analyzing how students’ typing patterns behave over time and demonstrates that typing patterns change over time due to learning resulting in several performance quality challenges.
Passphrase Authentication Based on Typing Style Through an Art 2 Neural Network
This study eliminates the need to collect impostor samples by employing an unsupervised and self-organizing artificial neural network algorithm, the Adaptive Resonance Theory 2 neural network, and therefore pushes the passphrase authentication technology one step closer to the realm of practical implementation.
Keystroke Dynamics on a Device with Touch Screen
Keystroke Dynamics has been heavily researched over many years. Despite the large activity there are few real world implementations using Keystroke Dynamics as an authentication mechanism. The change


Dynamic Identity Verification via Keystroke Characteristics
A research study is described which was conducted to determine the possibility of using keystroke characteristics as a means of dynamic identity verification, and results indicate significant promise in the temporal personnel identification problem.
Identity Verification Through Keyboard Characteristics
A reference profile is built to serve as a basis of comparison for future typing samples and has the capability of providing identity surveillance throughout the entire time at the keyboard.
Verifying Identity via Keystroke Characteristics
Basic data are presented and discussed that characterize the class of keystroke digraph latencies that are found to have good potential as static identity verifiers as well as dynamic identity verifier.
User unique identification
Traditionally, users have been authenticated by asking them to provide some form of password. This password has been stored securely in the computer and used to check the identity of the user at
User Identification via Keystroke Characteristics of Typed Names using Neural Networks
A method for identifying computer users by analysing keystroking patterns with neural networks and a simple geometric distance and preliminary results demonstrate complete exclusion of imposters and a reasonably low false alarm rate.
Experience of using a type signature password system for user authentication in a heavily used computing environment
This paper describes a user authentication system based around the user's type signature, a statistical measure of the user's typing style. It was tested on two heavily loaded computers. Disciplines
Keystroke Timing in Transcription Typing
Over the past few years, in collaboration with Jonathan Grudin, David Rumelhart, Donald Norman, and Serge Larochelle, I have been studying transcription typing in the laboratory. Typically, typists