Journal article
Cooperative feature level data fusion for authentication using neural networks
Neural Information Processing, Vol.8834, pp.578-585
2014
Abstract
In traditional research, data fusion is referred to as multi-sensor data fusion. The theory is that data from multiple sources can be combined to provide more accurate, reliable and meaningful information than that provided by a single data source. Applications in this field of study were originally in the military domain; more recently, investigations for its application in various civilian domains (eg: computer security) have been undertaken. Multi-sensor data fusion as applied to biometric authentication is termed multi-modal biometrics. The objective of this study was to apply feature level fusion of fingerprint feature and keystroke dynamics data for authentication purposes, utilizing Artificial Neural Networks (ANNs) as a classifier. Data fusion was performed adopting the cooperative paradigm, a less researched approach. This approach necessitates feature subset selection to utilize the most discriminatory data from each source. Experimental results returned a false acceptance rate (FAR) of 0.0 and a worst case false rejection rate (FRR) of 0.0006, which were comparable to—and in some cases, slightly better than—other research using the cooperative paradigm.
Details
- Title
- Cooperative feature level data fusion for authentication using neural networks
- Authors/Creators
- M. Abernethy (Author/Creator) - Murdoch UniversityS.M. Rai (Author/Creator) - Murdoch University
- Publication Details
- Neural Information Processing, Vol.8834, pp.578-585
- Publisher
- Springer Verlag
- Identifiers
- 991005541027607891
- Copyright
- 2014 Springer International Publishing Switzerland
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Journal article
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