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Hand Segmentation for Contactless Palmprint Recognition

  • Yusei SuzukiEmail author
  • Hiroya Kawai
  • Koichi Ito
  • Takafumi Aoki
  • Masakazu Fujio
  • Yosuke Kaga
  • Kenta Takahashi
Conference paper
  • 122 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12046)

Abstract

Extracting a palm region with fixed location from an input hand image is a crucial task for palmprint recognition to realize reliable person authentication under unconstrained conditions. A palm region can be extracted from the fixed position using the gaps between fingers. Hence, an accurate and robust hand segmentation method is indispensable to extract a palm region from an image with complex background taken under various environments. This paper proposes a hand segmentation method for contactless palmprint recognition. The proposed method employs a new CNN architecture consisting of an encoder-decoder model of CNN with a pyramid pooling module. Through a set of experiments using a hand image dataset, we demonstrate that the proposed method exhibits efficient performance on hand segmentation.

Keywords

Palmprint Segmentation Biometrics CNN Hand 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yusei Suzuki
    • 1
    Email author
  • Hiroya Kawai
    • 1
  • Koichi Ito
    • 1
  • Takafumi Aoki
    • 1
  • Masakazu Fujio
    • 2
  • Yosuke Kaga
    • 2
  • Kenta Takahashi
    • 2
  1. 1.Graduate School of Information SciencesTohoku UniversitySendaiJapan
  2. 2.Hitachi Ltd.YokohamaJapan

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