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Palmprint Recognition across Different Devices
AbstractIn 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.
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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.View more citation formats
Jia W, Hu R-X, Gui J, Zhao Y, Ren X-M. Palmprint Recognition across Different Devices. Sensors. 2012; 12(6):7938-7964.Chicago/Turabian Style
Jia, Wei; Hu, Rong-Xiang; Gui, Jie; Zhao, Yang; Ren, Xiao-Ming. 2012. "Palmprint Recognition across Different Devices." Sensors 12, no. 6: 7938-7964.