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Hand-geometry recognition using entropy-based discretization
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AI
DOI：10.1109/TIFS.2007.896915.png)
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Abstract
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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.
Keywords:
biometrics
feature discretization
feature representation
hand geometry
personal recognition
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8
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5.3K
Citations:
2.3W
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