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

Abstract

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.

Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation

“…Zhu et al [27] proposed CycleGAN, which translates images from the source domain to the target domain with the cycle consistency loss, and this method shows remarkable results in the style transfer task. Researchers have extended unpaired image-to-image translation for several applications [51][52][53][54][55][56][57][58] to address issues such as data imbalance, lack of diversity, and limitation in collecting real paired dataset.…”

Restoration of Motion Blurred Image by Modified DeblurGAN for Enhancing the Accuracies of Finger-Vein Recognition

“…Subsequently, training was conducted by dividing the original image into an N×N size, and the probability that the final input image was the vein pattern was calculated. Song et al [ 23 ] and Noh et al [ 24 , 25 ] proposed a shift-matching finger-vein recognition method using a composite image. Qin et al [ 26 ] proposed a finger-vein verification method that combined a CNN and long short-term memory (LSTM).…”