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Volume 8 Issue 5
May  2021

IEEE/CAA Journal of Automatica Sinica

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Xi Chen, Ming Yu, Feng Yue and Bin Li, "Orientation Field Code Hashing: A Novel Method for Fast Palmprint Identification," IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 1038-1051, May 2021. doi: 10.1109/JAS.2020.1003186
Citation: Xi Chen, Ming Yu, Feng Yue and Bin Li, "Orientation Field Code Hashing: A Novel Method for Fast Palmprint Identification," IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 1038-1051, May 2021. doi: 10.1109/JAS.2020.1003186

Orientation Field Code Hashing: A Novel Method for Fast Palmprint Identification

doi: 10.1109/JAS.2020.1003186
Funds:  This work was supported in part by the National Natural Science Foundation of China (61806071), the Natural Science Foundation of Hebei Province (F2019202464, F2019202381), the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) of China (201900043), Hebei Provincial Education Department Youth Foundation (QN2019207), and the Technical Expert Project of Tianjin (19JCTPJC55800, 19JCTPJC57000)
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  • For a large-scale palmprint identification system, it is necessary to speed up the identification process to reduce the response time and also to have a high rate of identification accuracy. In this paper, we propose a novel hashing-based technique called orientation field code hashing for fast palmprint identification. By investigating hashing-based algorithms, we first propose a double-orientation encoding method to eliminate the instability of orientation codes and make the orientation codes more reasonable. Secondly, we propose a window-based feature measurement for rapid searching of the target. We explore the influence of parameters related to hashing-based palmprint identification. We have carried out a number of experiments on the Hong Kong PolyU large-scale database and the CASIA palmprint database plus a synthetic database. The results show that on the Hong Kong PolyU large-scale database, the proposed method is about 1.5 times faster than the state-of-the-art ones, while achieves the comparable identification accuracy. On the CASIA database plus the synthetic database, the proposed method also achieves a better performance on identification speed.

     

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    Highlights

    • We use a gradient-based orientation field to obtain continuous orientation representations within an acceptable time-frame, and propose a double-orientation encoding method to make the orientation codes more accurate and stable.
    • We propose a window-based feature measurement, by which the process of position translations can be removed for speeding up the searching.
    • We evaluate the performance of accuracy and speed on two large-scale databases, namely, Hong Kong PolyU large-scale database and CASIA palmprint database plus a synthetic database. We compare the proposed method with existing hashing-based methods, as well as state-of-the-art palmprint identification approaches. The results demonstrate the obvious advantages of the proposed method over previous works.

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