Finger-vein Image Quality Assessment Based on light-CNN

Abstract

Low-quality finger vein images seriously affect the performance of the recognition system. Aiming at filtering high- and low-quality finger vein images, this paper presents a finger-vein image quality assessment method based on light-CNN (convolutional neural network). The previous quality assessment method aims to establish a model that is consistent with the evaluation effect of the human visual system. In contrast, our model further evaluates the quality of the finger vein image for the purpose of distinguishing whether the image contains rich and stable vein characteristics. The rich and stable vein characteristics in finger vein images play a more important role in practical applications. First, the finger vein image is automatically annotated based on a traditional finger vein image quality assessment method; secondly, the finger vein image is cut into image blocks to expand the training set, and the image block instead of the entire image is input to the CNN; then, the network and its variants are trained; in the final test, the average quality score of multiple image blocks from an image is the final quality score of the image. The experimental results show that the proposed light-CNN accurately identifies high and low quality images and significantly outperforms existing approaches in terms of distinguishing whether there are rich and stable vein characteristics in the image.