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- W2283127861 abstract "AbstractOver the past ten years, there has been a growing interest in hand-based recognition in biometric technology systems. In this paper, we investigated the application of random decision tree forests for hand identification using geometric hand measurements. We evaluated and compared the performance of the proposed method using out-of-bag validation and 10-fold cross validation in terms of identification. We also studied the impact of the forest size on the performance. The experimental results showed significant improvement over single decision trees, rule-based and nearest-neighbor machine learning algorithms.KeywordsBiometricsHand RecognitionMachine LearningDecision TreesRandom ForestsGeometric Features" @default.
- W2283127861 created "2016-06-24" @default.
- W2283127861 creator A5057893924 @default.
- W2283127861 date "2013-01-01" @default.
- W2283127861 modified "2023-09-27" @default.
- W2283127861 title "Enhanced Hand Shape Identification Using Random Forests" @default.
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- W2283127861 doi "https://doi.org/10.1007/978-3-642-42042-9_55" @default.
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