双Gabor滤波器手掌静脉识别网络  

Double Gabor-filter palm vein recognition network

作　　者：吴微[1] 张源 林森 陈希爱 李云鹏 Wu Wei;Zhang Yuan;Lin Sen;Chen Xiai;Li Yunpeng(School of Information Engineering,Shenyang University,Shenyang 110044,China;School of Automation and Electrical Engineering,Shenyang Ligong University,Shenyang 110159,China;Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China)

机构地区：[1]沈阳大学信息工程学院,沈阳110044 [2]沈阳理工大学自动化与电气工程学院,沈阳110159 [3]中国科学院沈阳自动化研究所,沈阳110016

出　　处：《中国图象图形学报》2024年第9期2753-2763,共11页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61903358);中国博士后基金项目(2019M651142);辽宁省自然科学基金项目(2021-BS-023);中国科学院青年创新促进会基金项目(2022196)。

摘　　要：目的基于手掌静脉的身份识别需要在近红外光下采集手掌血管图像,安全性高。开放环境下的非接触式采集,相对于传统的将手掌放到采集箱体内固定栓上的采集方式更受用户欢迎。但开放环境带来的可见光干扰和非接触拍摄带来的图像旋转、平移、比例缩放、光照改变使得识别具有挑战性。针对以上两个难点,研究了一种基于非监督卷积神经网络的方法。方法在卷积层中结合主成分分析(principal component analysis,PCA)滤波器提取主元信息,减少由于可见光引起的噪声影响;以固定尺寸Gabor滤波器为多尺度自适应Gabor滤波器提供先验知识,克服图像因几何与光照改变对识别产生的干扰,用以增强掌脉稳定特征,提升识别性能,再以二值化方式降低数据量,最后使用自适应K近邻(K-nearest neighbors,KNN)的变体分类器进行分类识别。结果采用自建图库、同济图库和PolyU-NIR图库进行实验分析,在3个图库中的等误率分别为0.2899%、0.2113%和0.1586%,误拒率和误识率分别为0.0027和2.3188、0.0023和1.2821、0.0000和1.5962。结论与传统方法以及经典网络方法相比,该方法能有效提高识别准确率,适用于对安全性要求较高的场合进行身份识别,具有实用价值。Objective Palm vein recognition takes advantage the stability and uniqueness of human palm vein distribution for identification.The palm vein is hidden under the epidermis and cannot be photographed under visible light,and the replication of its complex structure presents difficulty.A severed palm or corpse fails certification because blood has stopped flowing,which makes palm vein recognition suitable for high-security applications.The noncontact collection in an open environment is more popular than the traditional collection method of placing the palm on a fixed bolt in the collection box.However,the opacity,inhomogeneity,and anisotropy of the skin tissue covering the palm vein cause scattering of near-infrared light.The visible light in open environments aggravates scattering and increases noise,which result in vague palm vein imaging in some people.The noncontact acquisition method increases the intraclass difference of the same sample increase due to rotation,translation,scaling,and illumination in multiple shots.The above difficulties make recog⁃nition challenging.A method based on unsupervised convolutional neural network was studied with aim of addressing such difficulties.Method Given that this paper requires a benchmark library to train the self-built network,a palm vein image library containing 600 images from 100 volunteers was established.Then,the program parameters were trained and adjusted using the self-built library.After several training adjustments,the optimal\and the trained network parameters were acquired.In image preprocessing,the region of interest(ROI)was extracted from all the palm vein images collected.First,the original palm vein image was denoised and binarized via low-pass filtering.Then,the palm contour was extracted via the dilation method in binary morphology.The obtained palm contour was refined into a single pixel,and the palm vein ROI was extracted using a method mentioned in literature.The extracted palm-vein ROI was adjusted based on the corre⁃sponding pixel size,and the

关 键 词：生物特征识别 手掌静脉识别 非接触 近红外光图像 卷积神经网络(CNN) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]