# Finger-Vein Verification Based on LSTM Recurrent Neural Networks
###### Abstract
The representative works include wide line detector (WLD) [13], Gabor filters [3; 18; 19; 20; 21], and matched filters [22]. Some researchers observe that the cross-sectional profile of a vein pattern shows the attribute of valley shape. Therefore, many models are built to detect the valley for vein pattern extraction. For instance, the curvature is sensitive to valley, so various approaches are proposed to enhance the vein patterns by computing mean curvature [14], difference curvature [23], and maxim curvature [15] of pixels in an image. In [24; 25; 26; 27], the vein patterns are detected by computing the depth of the valley. In the region growth approach [27], both depth and symmetry of valley are combined to extract vein pattern. Recently, according to the anatomical knowledge, some characteristics of finger-vein structure, e.g., directionality, continuity, width variability, smoothness, and solidness are taken into account for finger-vein texture extraction in [28]. (2) Deep learning-based segmentation approaches. Unlike handcrafted approaches, the deep learning-based approaches are capable of extracting the vein patters from a raw image without the manual attribute distribution assumption and have shown promising performance in medical image segmentation such as neuronal membrane segmentation [29], prostate segmentation [30], retinal blood vessels [31], and brain image segmentation [32]. In work [33], the Convolutional Neural Network (CNN) model is firstly employed for finger-vein segmentation, and outperforms handcrafted feature-based approaches in terms of verification errors improvement. In their work, the pixels are automatically labeled and a patch-based dataset is built for CNN training. For testing, an image is split into various patches and each patch is put into the CNN to predict the probability of its center point being a vein pattern. The approaches described above achieve good performance on some finger-vein recognition tasks, but they suffer from the following problems. For example, existing handcrafted approaches segment vein pattern based on assumptions. However, these assumptions are not always effective to detect the finger-vein patterns because some vein pixels may be created by more complicated distributions than valleys or straight lines. Also, they explicitly extract some vein features by an image processing method, which might discard relevant information about the finger-vein pattern. In addition, they do not get any prior knowledge from the different images as they segment each image independently from the others. For the deep learning-based approach [33], these problems are alleviated to an extent because it directly uncovers hierarchical features from raw images to minimize its decision errors on vein patterns without the attribute distribution assumptions. Meanwhile, rich prior knowledge is harnessed by training it on a huge patch-based training data from different images. However, these approaches, including CNN in [33], segment each pixel independently based on a predefined neighborhood region (e.g., patch) instead of considering spatial dependencies among the closed pixels. Factually, finger-vein vein patterns extend from finger root to fingertip, and show clear direction and good connectivity [34]. Therefore, there exists spatial dependencies among the closed vein pixels. So, the performances of these existing approaches are still limited for finger-vein texture pattern extraction. Recurrent neural networks have shown powerful capacity for the representation of long-term dependency information and have been successfully applied to human activity [35; 36], speech recognition [37], and handwriting recognition [38]. In recent years, LSTM networks [39] as the most successful extensions of recurrent neural networks have received more and more attention.