Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors

Abstract

Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods.

Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor

“…The captured finger images are binarized, and the binarized images are used to restore the collapsed areas through the convex hull algorithm [ 34 ] to extract accurate finger regions. Then, a 4 × 20 mask is used to detect the upper and lower boundaries to extract the finger regions more accurately than [ 18 , 32 ] (step 2 of Figure 1 ). After this, the in-plane rotation of the extracted finger region is calibrated (step 3) to obtain the final ROI image for CNN input (step 4).…”

“…In this research, to solve these problems, in-plane rotation compensation was conducted on the input finger images. As shown in Figure 2 d, for an accurate rotation angle measurement in the obtained finger images, a 4 × 20 mask was used to detect the upper and lower boundaries of finger-veins, as shown in the red-colored lines of Figure 3 b [ 18 , 32 ]. After this, the two-dimensional moment of the finger region between these two boundaries is calculated and used for in-plane rotation compensation [ 35 ].…”

A Systematic Review of Finger Vein Recognition Techniques

“…In addition, conventional approaches require complex and complicated preprocessing and feature extraction, steps which need much more processing time and effort. To alleviate these problems, Hyung et al [126] proposed a robust deep CNN model and an error rate of 0.396 was attained on a good quality database. Moreover, Gesi et al [127] applied a convolutional neural network (CNN) finger vein recognition method and achieved almost 100% accuracy, obtaining a very low error rate of 0.21.…”

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

“…Biometrics in both physiological and behavioral forms are delivering an efficient platform for security metrics [ 1 ]. Physiological biometrics include fingerprint recognition [ 2 ], finger vein pattern recognition [ 3 ], face recognition [ 4 ], iris recognition [ 5 ], and palmprint recognition [ 6 ]. Iris recognition has been proven as innovative reliable biometric widely used in security, authentication, and identification systems [ 7 ].…”