# Finger-Vein Verification Based on LSTM Recurrent Neural Networks
###### Abstract  
Finger-vein biometrics has been extensively investigated for personal verification. A challenge is that the finger-vein acquisition is affected by many factors, which results in many ambiguous regions in the finger-vein image. Generally, the separability between vein and background is poor in such regions. Despite recent advances in finger-vein pattern segmentation, current solutions still lack the robustness to extract finger-vein features from raw images because they do not take into account the complex spatial dependencies of vein pattern. This paper proposes a deep learning model to extract vein features by combining the Convolutional Neural Networks (CNN) model and Long Short-Term Memory (LSTM) model. Firstly, we automatically assign the label based on a combination of known state of the art handcrafted finger-vein image segmentation techniques, and generate various sequences for each labeled pixel along different directions. Secondly, several Stacked Convolutional Neural Networks and Long Short-Term Memory (SCNN-LSTM) models are independently trained on the resulting sequences. The outputs of various SCNN-LSTMs form a complementary and over-complete representation and are conjointly put into Probabilistic Support Vector Machine (P-SVM) to predict the probability of each pixel of being foreground (i.e., vein pixel) given several sequences centered on it. Thirdly, we propose a supervised encoding scheme to extract the binary vein texture. A threshold is automatically computed by taking into account the maximal separation between the inter-class distance and the intra-class distance. In our approach, the CNN learns robust features for vein texture pattern representation and LSTM stores the complex spatial dependencies of vein patterns. So, the pixels in any region of a test image can then be classified effectively. In addition, the supervised information is employed to encode the vein patterns, so the resulting encoding images contain more discriminating features. The experimental results on one public finger-vein database show that the proposed approach significantly improves the finger-vein verification accuracy. biometrics; finger-vein verification; deep learning; convolutional neural network; representation learning +
Footnote †: journal: Applied Soft  
categories. (1) Extrinsic characteristics (e.g., fingerprints, palm-print, face, voice, signature); (2) Intrinsic characteristics (e.g., finger-vein, palm-vein, hand-vein). The extrinsic characteristics are prone to be attacked because faked face and fingerprint can successfully cheat the verification system [11]. As the intrinsic characteristics such as finger-vein conceal the skin and not easily copied and forged, they show high security and privacy in practical application. However, vein verification faces serious challenges. In practical applications, various factors such as environmental illumination [12; 13; 14], ambient temperature [3; 14; 15], light scattering [16; 17], and user behavior [12; 13] affect the finger-vein image quality. Generally, these factors are not controlled, so many capturing images not only contain vein patterns but also noise and irregular shadowing. Generally, the separability between the vein and non-vein patterns is poor in the regions associated with noise and irregular shadowing. Performing matching from such regions degrades the verification accuracy. To solve this problem, many segmentation-based methods are proposed to segment robust vein network for finger-vein recognition. Broadly, they can be categorized into two groups. (1) Handcraft-based segmentation approaches. In this category, researchers employed the existing mathematical models to detect vein features based on attribute assumptions such as valleys and straight-lines. For example, they assume that the vein patterns can be approximated to line-like texture in a predefined neighborhood region and the descriptors such as Gabor filters are proposed to extract the vein pattern.