Phase-Based Palmprint Identification with Convolutional Sparse Coding

Luis Rafael Marval-Perez, Koichi Ito, Takafumi Aoki

Research output: Contribution to journalArticlepeer-review


Phase-based image matching has shown high recognition accuracy in palmprint verification. The algorithm compares a pair of palmprint images by extracting local phase features from the images and computing local correlation functions between them. A major drawback of this algorithm is its high computational cost associated with the evaluation of local correlation functions. This needs to be addressed, especially in the case of one-to-many comparisons required for palmprint identification. The problem becomes increasingly severe as the number of enrolled images increases. In this paper, we propose a novel palmprint identification algorithm with low computational complexity, which employs a sparse representation of enrolled phase features (i.e., phase templates) to evaluate local correlation functions. For this purpose, we also develop an efficient Convolutional Sparse Coding (CSC) algorithm that can derive a compact representation of phase templates. The proposed method reduces the computational cost of phase-based palmprint identification without significant degradation of recognition performance. Our experiments using public databases clearly demonstrate the advantage of the proposed method over conventional methods.

Original languageEnglish
Pages (from-to)424-438
Number of pages15
JournalIEEE Transactions on Biometrics, Behavior, and Identity Science
Issue number3
Publication statusPublished - 2022 Jul 1


  • Convolutional sparse coding
  • biometrics
  • palmprint identification
  • phase correlation
  • phase features
  • phase-based image matching

ASJC Scopus subject areas

  • Instrumentation
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Artificial Intelligence


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