IIUM Repository

Evaluation of classifiers in a pressure and latency-based typing biometric system

Moez Khan, Imran and ., Imama and Ushama, K. M. and Aibinu, Abiodun Musa and Lai, Weng Kin (2011) Evaluation of classifiers in a pressure and latency-based typing biometric system. In: 2011 4th International Conference on Mechatronics: Integrated Engineering for Industrial and Societal Development (ICOM 2011), 17-19 May, 2011, Kuala Lumpur, Malaysia.

[img] PDF
Restricted to Repository staff only

Download (109kB) | Request a copy

Abstract

System authentication in present time relies on validation by some sort of a password or Personal Identification Number (PIN). However, if an intruder discovers this password or PIN, the user’s account can be easily compromised. Biometric systems rely on user authentication based on some physical or behavioral attribute. Typing biometrics is a behavioral biometric authentication system that seeks to identify users based on typing behavior and style, similar to the way that a signature identifies a person based on handwriting. In this paper, Microsoft’s newly prototyped Pressure Sensitive Keyboard (PSK) has been used to explore pressure and latency based typing biometrics. Statistical and neural network classifiers are used for user identification on testing samples and compared to evaluate their efficiency.

Item Type: Conference or Workshop Item (Full Paper)
Additional Information: 6472/1776
Uncontrolled Keywords: Typing Biometrics, Classification Technqiues, Pattern Recognition, Artificial Neurel Network, Pressure Sensitive Keyboard
Subjects: T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Dr Abiodun Musa Aibinu
Date Deposited: 09 Sep 2011 11:38
Last Modified: 29 Dec 2011 11:16
URI: http://irep.iium.edu.my/id/eprint/1776

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year