@inproceedings{1ac28788a54c4cdfbb02a2f7cabb54ca,
title = "Enhanced hand shape identification using random forests",
abstract = "Over 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.",
keywords = "Biometrics, Decision trees, Geometric features, Hand recognition, Machine learning, Random forests",
author = "El-Alfy, \{El Sayed M.\}",
year = "2013",
doi = "10.1007/978-3-642-42042-9\_55",
language = "English",
isbn = "9783642420412",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "441--447",
booktitle = "Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings",
edition = "PART 2",
}