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 | |
| Publication status | Published - 2011 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Artificial Intelligence
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A multiple layer fusion approach on keystroke dynamics. / Teh, Pin Shen; Teoh, Andrew Beng Jin; Tee, Connie; Ong, Thian Song.
In: Pattern Analysis and Applications, Vol. 14, No. 1, 01.01.2011, p. 23-36.Research output: Contribution to journal › Article
TY - JOUR
T1 - A multiple layer fusion approach on keystroke dynamics
AU - Teh, Pin Shen
AU - Teoh, Andrew Beng Jin
AU - Tee, Connie
AU - Ong, Thian Song
PY - 2011/1/1
Y1 - 2011/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79251595640&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79251595640&partnerID=8YFLogxK
U2 - 10.1007/s10044-009-0167-9
DO - 10.1007/s10044-009-0167-9
M3 - Article
AN - SCOPUS:79251595640
VL - 14
SP - 23
EP - 36
JO - Pattern Analysis and Applications
JF - Pattern Analysis and Applications
SN - 1433-7541
IS - 1
ER -