CNN based Finger Region Segmentation for Finger Vein Recognition

Finger region segmentation is an important step in a biometric finger vein recognition toolchain. Its aim is to separate the finger region from background and all other objects of the image. So far, finger region extraction for finger vein recognition systems has mainly used classical image processing based systems. In this work three state-of-the art convolutional neural network (CNN) based architectures for segmentation, namely Mask R-CNN, CCNet and HRNet, are evaluated. A major advantage of the presented CNN-based approach compared to classic image processing approaches is that the images neither have to be preprocessed nor any parameters have to be optimized. All that is required is a sufficient number of already segmented finger vein images for training.