Home > Published Issues > 2023 > Volume 14, No. 3, 2023 >
JAIT 2023 Vol.14(3): 431-443
doi: 10.12720/jait.14.3.431-443

A New Refined-TLBO Aided Bi-Generative Adversarial Network for Finger Vein Recognition

Hossam L. Zayed 1, Heba M. Abdel Hamid 1,*, Yasser M. Kamal 2, and Abdel Halim A. Zekry 3
1. Electrical Engineering Department, Benha Faculty of Engineering, Benha University, Benha, Egypt;
Email: hossam.zayed@bhit.bu.edu.eg (H.L.Z.)
2. Computer Science Department , Faculty of Computing and Information Technology, Arab Academy for Science Technology and Maritime Transport (AASTMT), Egypt; Email: dr_yaser_omar@yahoo.com (Y.M.K.)
3. Electronics and Communications Department, Faculty of Engineering, Ain Shams University, Egypt;
Email: aaazekry@hotmail.com (A.H.A.Z.)
*Correspondence: heba.hassan@bhit.bu.edu.eg (H.M.A.H.)

Manuscript received October 18, 2022; revised November 28, 2022, accepted December 11, 2022; published May 10, 2023.

Abstract—Finger vein recognition is a biometric authentication scheme for analyzing human finger vein patterns. Over the past few decades, Convolutional Neural Networks (CNN) have been widely used for finger vein recognition. However, the conventional issues of CNN are remaining unsolved which are translation invariance and the lack of considerations of position and orientation, thus unable to obtain a large recognition rate. In addition, pre-processing for all kind of finger vein images lead to extra overhead and increases the time for finger vein recognition. In this paper, we proposed a Bi-Generative Adversarial Network (Bi-GAN) with Teaching Learning Based Optimization (TLBO) for finger vein recognition. GAN is an architecture that can use CNNs and are really powerful in learning the underlying data distribution. Further, GAN has been applied previously for this application, but still, it has some serious issues such as hyper-parameters selection, and insufficiency for large feature extraction. Our proposed Bi-GAN with TLBO approach is involved four processes: (1) Image Quality Assessment (IQA), (2) Preprocessing, (3) Feature extraction, (4) Feature matching. We extract texture and soft biometric trait features by Bi-GAN and the parameters are optimized using the TLBO algorithm. In feature matching, we used Canberra Coefficient (CE). Experiments are conducted on the SDUMLA public database that exhibits the efficiency of the proposed Bi-GAN and TLBO in finger vein recognition. The results proved that the proposed approach is superior as analyzed to the CNN, GAN, and Bi-GAN approaches and gives the improvement in the accuracy of recognition.
Keywords—finger vein recognition, bi-generative adversarial networks, teaching learning based optimization, image quality assessment, texture, and soft biometric trait features extraction

Cite: Hossam L. Zayed, Heba M. Abdel Hamid, Yasser M. Kamal, and Abdel Halim A. Zekry, "A New Refined-TLBO Aided Bi-Generative Adversarial Network for Finger Vein Recognition," Journal of Advances in Information Technology, Vol. 14, No. 3, pp. 431-443, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.