Finger-Vein Recognition Based on Densely Connected Convolutional Network Using Score-Level Fusion With Shape and Texture Images

Biometrics using finger-veins is a recognition method based on the shape of veins in fingers, and it has the advantage of difficulty to be forged. However, a shade is inevitably produced due to the bones and fingernails, and a change in illumination occurs when acquiring the images of finger-veins. Previous studies have conducted finger-vein recognition using a single-type texture image or finger-vein segmented image (shape image). A texture image provides numerous features, but it is vulnerable to the changes in illumination during recognition and contains noises in regions other than the finger-vein region. A shape image is less affected by noises; however, the recognition accuracy is significantly reduced due to fewer features available and mis-segmented regions caused by shades. In this study, therefore, rough finger-vein regions in an image are detected to reduce the effect of mis-segmented regions, to complement the drawbacks of shape image-based finger-vein recognition. Furthermore, score-level fusion is performed for two output scores of deep convolutional neural network extracted from the texture and shape images, which can reduce the sensitivity to noise, while diverse features provided in the texture image are used efficiently. Two open databases, the Shandong University homologous multi-modal traits finger-vein database and Hong Kong Polytech University finger image database, are used for experiments, and the proposed method shows better recognition performance than the state-of-the-art method.