Improvement of confusion matrix for Hand Vein Recognition Based On Deep- Learning multi-classifier Decisions | ||||
| Arab Journal of Nuclear Sciences and Applications | ||||
| Article 14, Volume 54, Issue 4, October 2021, Page 133-146 PDF (881.76 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ajnsa.2021.70450.1460 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
NADIA MOSTAFA NAWWAR  1; Hany Kasban2; may salama3
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| 1Department of Nuclear Fuel Technology, Hot labs Center, Egyptian Atomic Energy Authority, Cairo 11787, Egypt. | ||||
| 2Engineering Department, NRC, Atomic Energy Authority, P. No. 13759, Inshas, Egypt | ||||
| 33Electrical Engineering Department, Faculty of Engineering at Shoubra, Banha University, Cairo 11787, Egypt | ||||
| Abstract | ||||
| In this paper, recognition of the hand vein patterns approach is proposed employing the Convolutional Neural Network (CNN). This approach is routinely well-learned in what way to get features from the main pattern using Region of Interest (ROI). Though, the poor quality of the hand vein image still attitudes an unlimited strain to the extension leads of its usability. Firstly, by applying the method of Generative adversarial networks (GAN) data augmentation the performance gain of adding GAN generated data exceeds that of adding more true images, and apply ROI in a hand vein image feature extraction is studied initially. Secondly, the suggested approach is tested on the data sets of hand veins to decrease the overfitting in the fully connecting layer of CNN which this model proves the most effective one. In total, 1575 hand vein images from 100 subjects are applied to authorize the proposed approach for hand vein. A high accuracy (>99.8%) and low False Rejection Rate(FRR) (<0.99%) were achieved by applying the suggested approach, when compared with the existing CNN classifiers, indicating the efficiency of the suggested approach. | ||||
| Keywords | ||||
| Biometric; Hand vein; CNN; GAN; FAR; FRR; and confusion matrix | ||||
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