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

作者：Junying Zeng, Fan Wang, Jianxiang Deng, Chuanbo Qin, Yikui Zhai, Junying Gan, Vincenzo Piuri · 发表于：IEEE Access · 年份：2020 · DOI：10.1109/access.2020.2984711 · 被引用次数：39 · 研究领域：Biometric Identification and Security、Forensic and Genetic Research、Dermatoglyphics and Human Traits

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...