Abstract
The biometrics based recognition systems proposed in the literature have not yet exploited user-specific dependencies in the feature level representation. This paper suggests and investigates the performance improvement of the existing biometric systems using the discretization of extracted features. The performance improvement due to the unsupervised and supervised discretization schemes is compared on verity of classifiers; KNN, naïve Bayes, SVM and FFN. The experimental results on the hand-geometry database of 100 users achieve significant improvement in the recognition accuracy and confirm the usefulness of discretization in biometrics systems.
Original language | English |
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Title of host publication | 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 |
Volume | 2 |
DOIs | |
Publication status | Published - 6 Aug 2007 |
Event | 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States Duration: 15 Apr 2007 → 20 Apr 2007 |
Conference
Conference | 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 |
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Country/Territory | United States |
City | Honolulu, HI |
Period | 15/04/07 → 20/04/07 |
Keywords
- Biometrics
- Feature discretization
- Feature representation
- Hand geometry
- Personal recognition
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering