• Corpus ID: 18202293

Touch-Screen Mobile-Device Data Collection for Biometric Studies

  title={Touch-Screen Mobile-Device Data Collection for Biometric Studies},
  author={W. Ciaurro and Bence Major and Devyani Panchal and Gonzalo Perez and Rajneil Rana and Manisha Rana and R. Reyes and Shandra Rodriguez and D. Sang and R. Valdez and D. Zulaga},
This paper focuses on data collection for keystroke biometric authentication studies. An experiment was performed on biometric keystroke characteristics of a person using a mobile device (nexus 5). Two types of input were collected -text and numeric entry from 10 different users. The purpose of the experiment was find ways to improve biometric security classification systems for future use, such as for the authentication of online test taking students and computer users. Included in the paper… 

Figures from this paper



Biometric System Design for Handheld Devices

A novel biometric modality system to identify and authenticate users that consists of unique components of biometric data based on common user gestures like scrolling, pinch to zoom, and clicking is devised.

Keystroke Biometric Studies on Password and Numeric Keypad Input

The keystroke biometric classification system described in this study outperforms 14 other systems evaluated in a previous study using the same raw input data and achieves an equal error rate on the numeric keypad input.

Recent Advances in the Development of a Long-Text-Input Keystroke Biometric Authentication System for Arbitrary Text Input

This study focuses on the development and evaluation of a new classification algorithm that halves the previously reported best error rate, using keystroke data from 119 users, and the varied performance over the population of users was analyzed.

Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

A classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen is proposed.

KeySens: Passive User Authentication through Micro-behavior Modeling of Soft Keyboard Interaction

This paper proposes a novel passive authentication method, and model the micro-behavior of mobile users’ interaction with their devices’ soft keyboard, showing that the way a user types reflects their unique physical and behavioral characteristics.