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Sensors 2012, 12(6), 7938-7964; doi:10.3390/s120607938

Palmprint Recognition across Different Devices

1
Institute of Nuclear Energy Safety Technology, Chinese Academy of Science, Hefei 230031, China
2
Institute of Intelligent Machines, Chinese Academy of Science, Hefei 230031, China
3
Department of Automation, University of Science and Technology of China, Hefei 230027, China
*
Author to whom correspondence should be addressed.
Received: 17 April 2012 / Revised: 21 May 2012 / Accepted: 22 May 2012 / Published: 8 June 2012
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
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Abstract

In this paper, the problem of Palmprint Recognition Across Different Devices (PRADD) is investigated, which has not been well studied so far. Since there is no publicly available PRADD image database, we created a non-contact PRADD image database containing 12,000 grayscale captured from 100 subjects using three devices, i.e., one digital camera and two smart-phones. Due to the non-contact image acquisition used, rotation and scale changes between different images captured from a same palm are inevitable. We propose a robust method to calculate the palm width, which can be effectively used for scale normalization of palmprints. On this PRADD image database, we evaluate the recognition performance of three different methods, i.e., subspace learning method, correlation method, and orientation coding based method, respectively. Experiments results show that orientation coding based methods achieved promising recognition performance for PRADD. View Full-Text
Keywords: biometrics; palmprint recognition; different devices; sensors biometrics; palmprint recognition; different devices; sensors
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Jia, W.; Hu, R.-X.; Gui, J.; Zhao, Y.; Ren, X.-M. Palmprint Recognition across Different Devices. Sensors 2012, 12, 7938-7964.

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