Biometric Recognition using Entropy-Based Discretization

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, naive 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.