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Research On Palmprint Recognition Based On Mechanism And Data

Published: 01 June 2024 Publication History

Abstract

Among the existing palmprint recognition technologies, traditional methods cannot adaptively extract multi-level features of palmprint images to better represent image information, and methods based on deep learning fail to make full use of prior knowledge existing in palmprint images. To solve this problem, A palmprint recognition method was proposed based on fusion mechanism and data, and constructs a palmprint recognition data model based on deep learning. The prior knowledge in palmprint images is extracted and added to the data model as mechanism knowledge to improve the model's ability to discriminate palmprint features. The multi-direction Gabor filter is used to optimize the prediction results and improve the recognition performance. The experimental results show that the recognition accuracy of the proposed method on CASIA palmprint database is 99.41%, which is better than the existing mainstream methods.

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CVDL '24: Proceedings of the International Conference on Computer Vision and Deep Learning
January 2024
506 pages
ISBN:9798400718199
DOI:10.1145/3653804
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 01 June 2024

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Author Tags

  1. Deep learning
  2. Gabor filter
  3. Mechanism
  4. Palmprint recognition

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  • Research-article
  • Research
  • Refereed limited

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  • Scientific research project supported by Education Department of Hunan Province
  • Chinese University Innovation Fund ---- IFLYtek University Wisdom Teaching Innovation Research project

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CVDL 2024

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