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Enhanced Hand Shape Identification Using Random Forests

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Neural Information Processing (ICONIP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8227))

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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.

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Author information

Authors and Affiliations

  1. College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    El-Sayed M. El-Alfy

Authors
  1. El-Sayed M. El-Alfy
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Editor information

Editors and Affiliations

  1. Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  2. The University of Tokyo, 7-3-1 Hongo, 113-8656, Bunkyo-ku, Tokyo, Japan

    Akira Hirose

  3. Institute of Automation, Key Laboratory of Complex Systems and Intelligence Science, Chinese Academy of Sciences, 100190, Beijing, China

    Zeng-Guang Hou

  4. Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, 440-746, Suwon, Korea

    Rhee Man Kil

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El-Alfy, ES.M. (2013). Enhanced Hand Shape Identification Using Random Forests. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_55

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  • DOI: https://doi.org/10.1007/978-3-642-42042-9_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-42041-2

  • Online ISBN: 978-3-642-42042-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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