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access icon free Low-cost biometric recognition system based on NIR palm vein image

  • Author(s): Wei Wu 1, 2 ; Stephen John Elliott 3 ; Sen Lin 2 ; Weiqi Yuan 4
    • View affiliations
  • Source: Volume 8, Issue 3, May 2019, p. 206 – 214
    DOI:  10.1049/iet-bmt.2018.5027 , Print ISSN 2047-4938, Online ISSN 2047-4946
© The Institution of Engineering and Technology
Received 16/03/2018, Accepted 08/11/2018, Revised 29/09/2018, Published 15/11/2018

Palm vein recognition is motivated by the advantages of high security and liveness detection, but its popularity is prevented by the cost of palm vein capture devices. This study proposes a low-cost and practical palm vein recognition system. First, the authors’ system captures near-infrared (NIR) palm vein image with complementary metal–oxide–semiconductor camera in lieu of an NIR charge-coupled device camera. The goal is to reduce the cost of palm vein capture devices greatly. Second, this study adopts thenar area on the palm as the region of interest (ROI) for further palm vein recognition. The goal is to get the rich vessel and avoid the effect of palmprint. Finally, the discriminate palm vein features are extracted based on Haar-wavelet decomposition and partial least squares algorithm on the ROI image. The goal is to increase the recognition accuracy, though the resolution of the image is low. A database with 1500 palm vein images from 250 samples is setup with the capture device. Experiments in the self-built database and a public database show the effectiveness of the scheme.

Inspec keywords: biometrics (access control); vein recognition; feature extraction; image recognition

Other keywords: high security; recognition accuracy; (NIR) palm vein image; discriminate palm vein features; palm vein capture devices; practical palm vein recognition system; NIR palm vein image; 1500 palm vein images; NIR charge-coupled device camera; low-cost biometric recognition system; authors; capture device; liveness detection; complementary metal–oxide–semiconductor camera

Subjects: Image recognition; Optical, image and video signal processing; Image sensors; Computer vision and image processing techniques

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