Finger Vein Verification Algorithm Based on Fully Convolutional Neural Network and Conditional Random Field

Abstract

Owing to the complexity of finger vein patterns in shape and spatial dependence, the existing methods suffer from an inability to obtain accurate and stable finger vein features. This paper, so as to compensate this defect, proposes an end-to-end model to extract vein textures through integrating the fully convolutional neural network (FCN) with conditional random field (CRF). Firstly, to reduce missing pixels during ROI extraction, the method of sliding window summation is employed to filter and adjusted with self-built tools. In addition, the traditional baselines are endowed with different weights to automatically assign labels. Secondly, the deformable convolution network, through replacing the plain counterparts in the standard U-Net mode, can capture the complex venous structural features by adaptively adjusting the receptive fields according to veins' scales and shapes. Moreover, the above features can be further mined and accumulated by combining the recurrent neural network (RNN) and the residual network (ResNet). With the steps mentioned above, the fully convolutional neural network is constructed. Finally, the CRF with Gaussian pairwise potential conducts mean-field approximate inference as the RNN, and then is embedded as a part of the FCN, so that the model can fully integrate CRF with FCNs, which provides the possibility to involve the usual back-propagation algorithm in training the whole deep network end-to-end. The proposed models in this paper were tested on three public finger vein datasets SDUMLA, MMCBNU and HKPU with experimental results to certify their superior performance on finger-vein verification tasks compared with other equivalent models including U-Net.

An ROI Extraction Method of Finger Vein Images Based on Large Receptive Field Gradient Operator for Accurate Localization of Joint Cavity

“…Qiu et al proposed a joint cavity search algorithm based on double sliding windows, which uses two windows for difference to eliminate the problem of affecting recognition [ 19 ] in the imaging process. Zeng et al [ 20 ] proposed that sliding method summation was employed to reduce missing pixels from finger ROIs. Yang et al [ 21 ] proposed a method in which the finger region was obtained using a fixed window and image standard deviation.…”

Raspberry Pi-Based Device for Finger Veins Collection and the Image Processing-Based Method for Minutiae Extraction

“…The first interesting work was presented in [2]. In the experimental phase the Authors used three publicly available datasets that are: SDUMLA-HMT, MMCBNU6000 and HKPU.…”

Anti-aliasing convolution neural network of finger vein recognition for virtual reality (VR) human–robot equipment of metaverse

“…FV-GAN model, a Generative Adversarial Network (GAN) model for finger vein based on Cycle-GAN architecture, is introduced in [ 20 ] to overcome the low quality image problem and insufficient data. The authors in [ 21 ] suggests a fully convolutional neural network, an extension of U-Net, and an embedded conditional random field as an end-to-end system for pixel-wise finger vein segmentation, and achieves effective performance in vein pattern segmentation results. R.S.…”