3D palmprint identification using blocked histogram and improved sparse representation-based classifier

  • Xuefei Bai
  • Zhaozong Meng
  • Nan Gao
  • Zonghua ZhangEmail author
  • David Zhang
Original Article


The three-dimensional (3D) palmprint-based biometrics has been an emerging approach for human recognition. However, it mainly concentrates on one-to-one verification and is not efficient for one-to-many identification. Especially, when there is a large number of samples in the database, obtaining both the accuracy and speed of recognition has been the major challenge. To handle these problems, we propose an improved sparse representation-based 3D palmprint identification scheme. The novelty of this investigation focuses on: (1) The histogram of blocked surface type and local phase of palmprint are integrated as one feature to reduce the dimensionality of 3D information and the complexity of the subsequent computations; and (2) by adding intermediate terms to the calculation of sparse coefficient, an improved sparse representation-based classifier is proposed to refine the classification results and enhance the accuracy of identification. Experimental studies demonstrate the effectiveness of the proposed scheme. Compared with other methods, the proposed solution can reduce the computational complexity and elevate the accuracy, speed, and robustness of identification. Moreover, the proposed 3D feature occupies small data volume, which makes the scheme more suitable for large-scale specimen identification applications.


Local phase Histogram Surface type (ST) Sparse representation-based classifier (SRC) 



This work was supported by the National Natural Science Foundation of China (51675160), the Talents Project Training Funds in Hebei Province (A201500503), and the Innovative and Entrepreneurial Talent Project Supported by Jiangsu Province (2016A377).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interests.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  1. 1.State Key Laboratory of Reliability and Intelligence of Electrical EquipmentHebei University of TechnologyTianjinChina
  2. 2.School of Mechanical EngineeringHebei University of TechnologyTianjinChina
  3. 3.School of Science and EngineeringThe Chinese University of Hong KongShenzhenChina

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