Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Journal article


Izadpanahkakhk, M., Razavi, S., Taghipour-Gorjikolaie, M., Zahiri, S. and Uncini, A. (2018). Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning. Applied Sciences. https://doi.org/10.3390/app8071210
AuthorsIzadpanahkakhk, M., Razavi, S., Taghipour-Gorjikolaie, M., Zahiri, S. and Uncini, A.
Abstract

Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier. In order to evaluate our proposed model, we computed the intersection over union (IoU) metric for ROI extraction along with accuracy, receiver operating characteristic (ROC) curves, and equal error rate (EER) for the verification task.The experiments demonstrated that the ROI extraction module could significantly find the appropriate palmprint ROIs, and the verification results were crucially precise. This was verified by different databases and classification methods employed in our proposed model. In comparison with other existing approaches, our model was competitive with the state-of-the-art approaches that rely on the representation of hand-crafted descriptors. We achieved a IoU score of 93% and EER of 0.0125 using a support vector machine (SVM) classifier for the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database. It is notable that all codes are open-source and can be accessed online.

Keywordsregion of interest extraction; palm print verification; deep learning; convolutional neural network; transfer learning; feature extraction
Year2018
JournalApplied Sciences
PublisherMDPI
ISSN2076-3417
Digital Object Identifier (DOI)https://doi.org/10.3390/app8071210
Web address (URL)https://www.mdpi.com/2076-3417/8/7/1210
Publication dates
Online23 Jul 2018
Publication process dates
Accepted02 Jul 2018
Deposited06 Jun 2024
Publisher's version
License
File Access Level
Open
Permalink -

https://openresearch.lsbu.ac.uk/item/975v8

Download files


Publisher's version
applsci-08-01210.pdf
License: CC BY 4.0
File access level: Open

  • 14
    total views
  • 7
    total downloads
  • 4
    views this month
  • 2
    downloads this month

Export as

Related outputs

A Novel Multiple Camera RGB-D Calibration Approach Using Simulated Annealing
Taghipour-Gorjikolaie, M., Volino, M., Rusbridge, C. and Wells, K. (2024). A Novel Multiple Camera RGB-D Calibration Approach Using Simulated Annealing. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3424412
Prediction of Nonsinusoidal AC Loss of Superconducting Tapes Using Artificial Intelligence-Based Models
Yazdani-Asrami, M., Taghipour-Gorjikolaie, M., Song, W., Zhang, M. and Yuan, W. (2020). Prediction of Nonsinusoidal AC Loss of Superconducting Tapes Using Artificial Intelligence-Based Models. IEEE Access. 8, pp. 207287 - 207297. https://doi.org/10.1109/access.2020.3037685