# FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling
## 2 Related Works
### Deep Learning Based Finger Vein Recognition  
With the breakthrough of big data and DL in several image processing tasks, such as classification [26], object detection [27], and digital image processing [28], the application of DL has also been considered for fast and automatic feature extraction from finger vein images [29; 30; 31; 32; 33; 34; 35; 36]. Due to the robustness of feature representations, a DL technique can be a novel candidate for finger vein recognition, irrespective of the shape and orientation of the vein patterns. Using large amounts of training samples, a DL algorithm such as the convolutional neural network (CNN) can provide powerful feature extraction capabilities with vein images and can quickly adapt to learning those feature representations. In [29], a densely connected convolutional neural network (DenseNet) was used for finger vein recognition where the matching score was generated by the score level fusion of shape and texture images. A similar study was investigated in [30], where a three-channel composite image was used as input to the DenseNet-based recognition network and provided good recognition performance. To achieve robust recognition performance from finger vein biometrics, the common CNN architecture has been extensively used in recent research [31; 32; 33]. Furthermore, to learn more robust features directly from the raw pixels of the finger vein image, the convolutional autoencoder (CAE) [34], recurrent neural networks (RNNs) [35], and generative adversarial networks (GANs) [36] have been researched as well. Although previous DL-based finger vein recognition techniques have offered promising recognition performance, they still suffer from relevant shortcomings, especially associated with mediocre feature extraction procedures. The finger vein images may include not only vein patterns but also irregular noise and intensity degradation due to the various finger shapes and muscles. Due to infirmly designed feature extraction networks in existing methods, the models cannot properly learn distinctive features of finger vein patterns, and thus, a unique representation of the vein patterns of each subject cannot be accessed, and this ultimately results in poor accuracy. Besides, currently available finger vein image databases acquired via infrared imaging offer significantly poor visual quality in the vein patterns. Thus, for higher recognition performance, poor image quality must be complemented by designing an effective preprocessing pipeline and a robust feature extractor. To overcome such limitations, in this paper, we propose FVR-Net, which is a novel CNN-based hybrid pooling network for finger vein recognition system that supports exquisite feature extraction to enable accurate recognition of finger vein images, regardless of the acquisition procedures of existing datasets. FVR-Net was designed to capture intricate vein features from the input images through several convolutional and subsampling layers. Unlike previous studies, FVR-Net introduces block-wise feature extraction with hybrid pooling where two subsampling layers (maxpooling and average pooling) in each block are placed in parallel after the convolutional layer, whose output activation maps are concatenated before passing features to another block. In hybrid pooling, the subsampling layers placed in parallel concurrently activate the most discriminative features of each input and provide excellent localization of those features. Upon forward pass, these blocks tend to extract multilevel and discrete interclass features of the input, which means they are able to uniquely represent the vein samples from different categories when subjected to classification with three fully connected layers (FCLs). In the whole process, the finger vein images are first subjected to preprocessing where ROI cropping and quality enhancement is achieved, along with proper segmentation of vein patterns from the background.