A multiple layer fusion approach on keystroke dynamics

Pin Shen Teh; Andrew Beng Jin Teoh; Connie Tee; Thian Song Ong

doi:10.1007/s10044-009-0167-9

A multiple layer fusion approach on keystroke dynamics

Pin Shen Teh, Andrew Beng Jin Teoh, Connie Tee, Thian Song Ong

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

Abstract

In this paper, we present a novel keystroke dynamic recognition system by means of a novel two-layer fusion approach. First, we extract four types of keystroke latency as the feature from our dataset. The keystroke latency will be transformed into similarity scores via Gaussian Probability Density Function (GPD). We also propose a new technique, known as Direction Similarity Measure (DSM), which measures the absolute difference between two sets of latency. Last, four fusion approaches coupled with six fusion rules are applied to improve the final result by combining the scores that are produced by GPD and DSM. Best result with equal error rate of 1.401% is obtained with our two-layer fusion approach.

Original language	English
Pages (from-to)	23-36
Number of pages	14
Journal	Pattern Analysis and Applications
Volume	14
Issue number	1
DOIs	https://doi.org/10.1007/s10044-009-0167-9
Publication status	Published - 2011 Feb

Bibliographical note

Funding Information:
This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University. (Grant Number: R112002105080020 (2009)).

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1007/s10044-009-0167-9

Cite this

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A multiple layer fusion approach on keystroke dynamics

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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