作　　者： ; ; ; ;

机构地区： 五邑大学信息工程学院

出　　处： 《中国图象图形学报》 2019年第6期902-913,共12页

摘　　要： 目的针对手动设计的手指静脉质量特征计算过程复杂、鲁棒性差、表达效果不理想等问题,提出了基于级联优化CNN(卷积神经网络)进行多特征融合的手指静脉质量评估方法。方法以半自动化方式对手指静脉公开数据库MMCBNU_6000进行质量标注并用R-SMOTE(radom-synthetic minority over-sampling technique)算法平衡类别;将深度学习中的CNN结构应用到手指静脉质量评估并研究了不同的网络深度对表征手指静脉质量的影响;受到传统方法中将二值图像和灰度图像结合进行质量评估的启发,设计了两种融合灰度图像和二值图像的质量特征的模型:多通道CNN(MC-CNN)和级联优化CNN(CF-CNN),MC-CNN在训练和测试时均需要同时输入二值图像和灰度图像,CF-CNN在训练时分阶段输入二值图像和灰度图像,测试时只需输入灰度图像。结果本文设计的3种简单CNN结构(CNN-K,K=3,4,5)在MMCBNU_6000数据库上对测试集图像的分类正确率分别为93.31%、93.94%、85.63%,以灰度图像和二值图像分别作为CNN-4的输入在MMCBNU_6000数据库上对测试集图像的分类正确率对应为93.94%、91.92%,MC-CNN和CF-CNN在MMCBNU_6000数据库上对测试集图像的分类正确率分别为91.44%、94.62%,此外,与现有的其他算法相比,CF-CNN在MMCBNU_6000数据库上对高质量测试图像、低质量测试图像、整体测试集图像的分类正确率均最高。结论实验结果表明,基于CF-CNN学习到的融合质量特征比现有的手工特征和基于单一静脉形式学习到的特征表达效果更好,可以有效地对手指静脉图像进行高、低质量的区分。 Objective Finger vein recognition, an emerging biometric identification technology, has attracted the attention of numerous researchers. However, the quality of several collected finger vein images is not ideal due to individual differences, changes in the collection environment, and differences in the performance of acquisition equipment. In a finger vein recognition system, low-quality images seriously affect feature extraction and matching, resulting in poor identification performance of the system. In an application scene that requires the establishment of a standard template library of personal finger vein information in real life, registered low-quality images seriously influence the use of the finger vein standard template library. Therefore, correct quality assessment after collecting finger vein images is necessary to filter low-quality images and select high-quality ones to be inputted to a finger vein recognition system or to register a finger vein standard template library. To address the problems of considerable computation complexity, weak robustness, and unsatisfactory expression and the issue that the hand-crafted finger vein quality characteristic is sensitive to various factors, we develop a finger vein quality assessment method. These problems are addressed via multi-feature fusion, which is primarily based on the cascaded fine-tuning convolutional neural network (CNN). Method Finger vein image quality assessment methods based on deep learning require many labeled finger vein images. However, existing finger vein image public databases only provide finger vein images and do not mark them for quality. Thus, the first step should be labeling. In this study, the public finger vein database MMCBNU_6000 is labeled for quality representation in a semi-automated manner. This manner is based on the calculation of the number of veins in a finger vein image, followed by manual correction. Such an annotation method is more accurate, time saving, and cost effective than a pure manual annotation method. How

关 键 词： 手指静脉质量评估 卷积神经网络 特征融合 多通道 级联优化

领　　域： []