Next Article in Journal
Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River
Next Article in Special Issue
Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition
Previous Article in Journal
An IOHMM-Based Framework to Investigate Drift in Effectiveness of IoT-Based Systems
Previous Article in Special Issue
Monocular Depth Estimation with Joint Attention Feature Distillation and Wavelet-Based Loss Function
Open AccessArticle

Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro, 1-gil, Jung-gu, Seoul 04620, Korea
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 524; https://doi.org/10.3390/s21020524
Received: 24 December 2020 / Revised: 9 January 2021 / Accepted: 10 January 2021 / Published: 13 January 2021
The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases—Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method. View Full-Text
Keywords: finger-vein recognition; camera position; finger position; lighting; unobserved database; heterogeneous database; domain adaptation; cycle-consistent adversarial networks; SDUMLA-HMT-DB; HKPolyU-DB finger-vein recognition; camera position; finger position; lighting; unobserved database; heterogeneous database; domain adaptation; cycle-consistent adversarial networks; SDUMLA-HMT-DB; HKPolyU-DB
Show Figures

Figure 1

MDPI and ACS Style

Noh, K.J.; Choi, J.; Hong, J.S.; Park, K.R. Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network. Sensors 2021, 21, 524. https://doi.org/10.3390/s21020524

AMA Style

Noh KJ, Choi J, Hong JS, Park KR. Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network. Sensors. 2021; 21(2):524. https://doi.org/10.3390/s21020524

Chicago/Turabian Style

Noh, Kyoung J.; Choi, Jiho; Hong, Jin S.; Park, Kang R. 2021. "Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network" Sensors 21, no. 2: 524. https://doi.org/10.3390/s21020524

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop