Ameva: An autonomous discretization algorithm
Expert Systems with Applications: An International Journal
Robust palmprint verification using 2D and 3D features
Pattern Recognition
Personal authentication using finger knuckle surface
IEEE Transactions on Information Forensics and Security
A new framework for adaptive multimodal biometrics management
IEEE Transactions on Information Forensics and Security
DHV image registration using boundary optimization
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Hand shape recognition based on coherent distance shape contexts
Pattern Recognition
A novel hand reconstruction approach and its application to vulnerability assessment
Information Sciences: an International Journal
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The hand-geometry-based recognition systems proposed in the literature have not yet exploited user-specific dependencies in the feature-level representation. We investigate the possibilities to improve the performance of the existing hand-geometry systems using the discretization of extracted features. This paper proposes employing discretization of hand-geometry features, using entropy-based heuristics, to achieve the performance improvement. The performance improvement due to the unsupervised and supervised discretization schemes is compared on a variety of classifiers: k-NN, naive Bayes, SVM, and FFN. Our experimental results on the database of 100 users achieve significant improvement in the recognition accuracy and confirm the usefulness of discretization in hand-geometry-based systems