基于支持向量机的近红外人脸与虹膜融合算法

提出了一种基于近红外人脸和虹膜的匹配层融合算法.该算法对近红外人脸运用小波变换结合二维主成分分析的特征提取方法和欧氏距离比较的匹配方法,对虹膜运用局部信息统计的分块编码方法和汉明距离比较的匹配方法,并在匹配层采用支持向量机策略对匹配分值进行融合,从而运用融合后的匹配分值进行决策.在一定规模的多模态数据库中进行了算法的验证,融合结果显示:基于支持向量机的近红外人脸和虹膜匹配层的融合,能够对较高的虹膜识别的准确率进一步提升,增强了系统识别率及鲁棒性.     

非接触式虹膜图像获取及特征提取方法的研究

报道了一种由使用者自行瞄准来实现虹膜图像非接触式获取的新方法,对获得的图像数据进行了虹膜纹理特征提取试验,验证了方法的可行性和有效性。通过设计几何光照明来辅助使用者定位瞄准,使眼睛处于前后合适的位置;并在摄像机镜头前面放置一片半透半反镜,帮助使用者观察自己眼睛的像来判断眼睛是否偏出摄像机的视场范围。通过该方法可以得到虹膜特征识别的图像数据。采用二维伽博小波对虹膜纹理进行特征提取,并计算两个虹膜特征码值中数值相等的特征位个数,对采集到的虹膜图像进行了特征匹配实验,得到的实验数据证实了该系统完全满足虹膜特征识别的要求。     

基于灰度曲面匹配的虹膜识别方法

目前的虹膜识别都是采用在图像上提取特征点,并将特征点编码为固定长度的特征数据用于匹配的方式。这种方式使虹膜识别系统易受攻击。为了进一步提高虹膜识别系统的安全性和识别速度,提出了一种基于灰度曲面匹配的虹膜识别方法。该方法抛弃了特征提取和编码等传统操作,在特征分析的基础上直接利用了灰度曲面匹配的思想,首先对两幅图像中的像素灰度做差,得到灰度差曲面,然后求出该灰度差曲面的方差。将此方差作为衡量两个虹膜特征曲面之间距离的依据,并据此判定两个虹膜是否来自同一只眼睛。在给定阈值为40的前提下,正确识别率为96.89%,识别时间为53.2 ms。实验结果证明,该方法识别准确率高,识别速度快。     

动车组列车序列图像快速对齐方法

线阵相机具有高敏感性、高分辨率和高动态性等特点,常用于运动物体成像。在动车组列车运行故障图像动态检测中,由于列车运行速度不是理想匀速通过线阵相机,所拍摄的图像会在列车运行方向上出现拉伸或压缩等变形现象,造成同一列车在不同时间所拍摄到的图像总数不一致,给后续目标自动定位、识别以及故障自动检测带来挑战。为解决图像之间未对齐的问题,提出了一种基于分块式的图像对齐方法。首先对图像进行分块处理、特征提取和匹配、特征量化,然后根据特征点之间的像素距离实现图像分块式校准,最后级联各个校准之后的图像块,完成目标图像与标准图像的对齐。实验结果表明,该方法对线阵相机所拍摄的动车组列车序列图像具有较好的对齐效果,可以精确定位序列图像中的目标。     

基于局部保持投影的掌纹识别

为了保持掌纹空间的局部结构,运用局部保持投影(LPP)方法进行掌纹识别.在小样本图像识别中,特征方程矩阵存在奇异性.传统的解决方法是运用主元分析(PCA)获得原样本的低维特征子空间,在该空间中运用LPP进行特征提取.由于PCA和LPP的投影标准本质上是不同的,PCA降维时丢失许多重要的判别信息.为了解决这个问题,提出运用三级小波变换、图像下抽样、图像分块求平均值三种方法降低掌纹空间的维数,在低维图像上应用LPP提取局部特征.计算特征矢量间的余弦距离进行掌纹匹配.运用PolyU掌纹图像库进行测试,结果表明,该算法的识别性能均优于PCA和PCA LPP.特征提取和匹配总时间小于0.1 S,具有快速、有效、易于实现等优点.     

虹膜识别技术及应用

虹膜识别技术由于具有稳定性好、错误接受率 (FAR)和错误拒绝率 (FRR)相等的指标最优、识别速度快等优点正逐步受到人们的重视。对虹膜纹理的特征、虹膜识别技术的发展和应用进行了较详细的介绍 ;通过将虹膜识别技术与其它生物识别技术相比较 ,对虹膜识别技术的优点进行了讨论。讨论了虹膜识别系统的构成 ,以及用二维Gabor小波和其它小波变换实现特征编码的方法和叙述了虹膜识别中所采用的一些算法     

基于线阵图像传感器的高分辨力单圈绝对式编码方法

为了提高测角分辨力,缩小码盘尺寸,并克服传统图像式编码器译码速度慢等缺点,提出了一种单圈绝对式高分辨力编码技术。通过分析码盘刻线与分辨力之间的关系,提出了绝对式单圈移位码编码方法;采用准直光源照射和线阵图像探测器识别的方法实现单圈码盘的译码方式;根据所提出的方法,设计了高分辨力码盘及译码电路。实验采用直径38mm、刻划有10位编码的单圈码道,成功实现了10位编码的译码。该编码方式较传统采用面阵图像探测器的编码方法具有更快的响应频率。     

基于单频2D-Gabor滤波器的虹膜识别算法研究

针对2D-Gabor滤波器设计的问题,提出了一种单频2D-Gabor滤波器设计方法。该方法在详细分析了2D-Gabor滤波器中各个参数意义的基础上,通过二分法的思想确定适合虹膜纹理的单一频率ω,再根据其它参数与其关系确定出各个参数取值,从而获得单一频率下的2D-Gabor滤波器。实验结果表明使用设计的单频2D-Gabor滤波器对虹膜图像进行特征提取可以使虹膜识别的识别率达到98.61%以上。使用单频2D-Gabor滤波器不仅可以提高特征提取速度,降低存储空间,同时也利于硬件实现。     

基于局部不变特征的目标自动识别

为快速、准确地识别图像中的目标,提出一种结合图像熵和加速鲁棒特征算法的目标自动识别方法.首先,分块计算图像的信息熵,根据阈值筛选出纹理丰富区域.然后,结合Hessian矩阵和Harris算法提取纹理丰富区域的局部特征点.接着,计算特征向量并用主成分分析降低向量维数.最后,采用双向最近距离比例匹配算法进行分类,并用随机抽样一致算法剔除误匹配点.实验结果表明:对仿真数据库中带有视角、光照和尺度变化的图像,识别率分别为87.12%、75.31%和84.98%,平均识别时间分别为70.35ms、71.27ms、220.63ms;对含8956×6708像素的航空大面阵图像,正确匹配率为78.13%,识别时间为68.09s.本方法识别率和时间性能均优于加速鲁棒特征算法.     

MPEG-4静态纹理BQ模式编码算法的改进

根据MPEG-4静态纹理编码工具中所定义的解码过程,建立了与标准相容的静态纹理编解码框架,然后从零树符号的信息含义及其与编码层目标图像的一致性出发,研究MPEG-4 VTC工具中位平面模式编码的改进问题,提出一种基于符号分解的预测性嵌入式小波零树编码方法,并用SP-PEZW方法对彩色参考图像Lena进行了压缩和解码实验研究.实验表明,SP-PEZW方法在低比特率编码层上比PEZW有更好的压缩效率,同时对高分辨率编码层压缩比并无大的影响,甚至在高分辨率空间层以指定码率解码时图像质量略有提高.     

Optics based biohashing using joint transform correlator

Most authentication process uses password and personal identification numbers (PIN) for security purposes. In order to remove the problem of hacking or stealing of the password and PIN numbers, there has been an increased interest in the utilization of specific biometric feature of the user. Recently, biohashing systems have been introduced for automatic biometric recognition. In a biohashing system, biohash codes are generated using the feature of the biometric. A basic biohashing system involves two steps. First is the extraction of the feature from the input biometric image and second is the discretisation of the obtained feature vector by using ortho-normalized random numbers. In this paper, a new biohashing system has been proposed in which joint transform correlator (JTC) has been used for extraction of the specific feature of the biometric. In the enrolment process, a biohash code has been generated by using a single face image and then stored. In the verification process, this biohash code is matched with the verification codes for recognition purpose. The main advantage of the proposed biohashing method is the possibility of the optical implementation of the feature extraction of the face image. Experimental as well as simulation results have been given to validate the proposed technique. Normalized Hamming distance has been calculated to discriminate the genuine and impostor face images. By varying the dimension of the feature matrix, the study of the variation of the normalized Hamming distance with the density of the population has been undertaken. For the performance evaluation of the proposed technique the false rejection ratio (FRR) and false acceptance ratio (FAR) have also been calculated.     

尺度不变特征与几何特征融合的人耳识别方法

要提高人耳的识别率,关键是特征的提取与表达.尺度不变特征变换(SIFT)技术是局部点特征提取算法,在尺度空间寻找极值点,提取对图像的尺度和旋转变化具有不变性,对光照变化和图像变形具有较强的适应性的特征向量.尝试用SIFT技术来提取外耳图像的结构特征点以形成稳定的特征描述子,为了克服一幅图像中有多个局部描述子相似的问题,在SIFT特征描述子中融入一个耳廓几何特征.最后采用特征向量的欧氏距离作为两幅图像相似性度量标准进行人耳识别.在耳图像库七进行实验.结果表明,该方法不仅可以有效地提取人耳特征,通过少量特征可获得较高的识别率,而且对耳图像刚体变化具有较强的稳健性.     

A non-circular iris localization algorithm using image projection function and gray level statistics

Iris recognition technology identifies an individual from its iris texture with great precision. A typical iris recognition system comprises eye image acquisition, iris segmentation, feature extraction, and matching. However, the system precision greatly depends on accurate iris localization in the segmentation module. In this paper, we propose a reliable iris localization algorithm. First, we locate a coarse eye location in an eye image using integral projection function (IPF). Next, we localize the pupillary boundary in a sub image using a reliable technique based on the histogram-bisection, image statistics, eccentricity, and object geometry. After that, we localize the limbic boundary using a robust scheme based on the radial gradients and an error distance transform. Finally, we regularize the actual iris boundaries using active contours. The proposed algorithm is tested on public iris databases: MMU V1.0, CASIA-IrisV1, and the CASIA-IrisV3-Lamp. Experimental results demonstrate superiority of the proposed algorithm over some of the contemporary techniques.     

Localization of iris in gray scale images using intensity gradient

A new method of iris localization based on intensity value analysis is proposed in this paper. Iris recognition systems are mainly dependent on the performance of iris localization processing. Steps after localization involve normalization, feature extraction and matching. These steps are based on the accuracy and efficiency of localization of iris in human eye images. In the proposed scheme, the inner boundary of iris is calculated by finding the pupil center and radius using two methods. In the first method, selected region is adaptively binarized and centroid of the region utilized for obtaining pupil parameters. Edges are processed to detect radius and center of pupil during the second method. For outer iris boundary, a band is calculated within which iris outer boundary lies. Signals in one dimension are picked up along radial direction within determined band at different angles. Three points with maximum gradient are selected from each signal. Redundant points are deleted using Mahalanobis distance and remaining points are used to obtain the outer circle of the iris. Points for upper and lower eyelids are found in the same way as the iris outer boundary. Selected points are then statistically fitted to make parabolas and lastly eyelashes are removed from the image to completely localize the iris. Experimental results show that proposed method is very efficient and accurate.     

基于多尺度特征提取与多元回归分析的人脸识别

为提高人脸识别的正确率,提出了一种改进的特征提取及分类算法。首先采用Contour-let变换对人脸图像进行多尺度分解,然后由低频子带和各尺度各方向的高频子带得到人脸的特征值,并将它们组合成多尺度特征向量,再应用多元回归分析方法进行人脸识别。由于多尺度特征向量不仅反映了整幅图像的全局特征,还反映了图像各种尺度下的边缘、纹理等奇异特征,因此具有更多的鉴别信息;多元回归分析则充分考虑了同一总体的各样本间的强线性关系。在ORL人脸库上的实验显示人脸识别率达97．78％,优于其他的方法。     

A data hiding approach for the self-security of iris recognition

Attacks to biometric data are the primary danger to the self-security of biometrics. To improve the iris feature template data security, a data hiding approach based on bit streams is proposed, in which an iris feature template is embedded into a face image. The proposed approach is applicable to present dominant techniques of iris recognition. With the low computation cost and the zero decoding-error-rate, this data hiding approach, embedding target biometrie data into other biometrie data for improving the security of target data in identity recognition, data storage and transmission, can deceive attackers more effectively. Furthermore, it does not degrade the iris recognition performances. Experimental results prove that the proposed approach can be used to protect iris feature templates and enhance the security of the iris recognition system itself.     

Facial expression recognition based on two-stage features extraction

Facial expression recognition plays an important role in a variety of real-world applications such as human–computer interaction, robot control, smart meeting, and visual surveillance. One critical step for facial expression recognition is to accurately extract emotional features. In this article, a facial expression recognition approach based on two-stage local facial textures extraction is proposed. At the first stage, we use the threshold local binary pattern to transform a facial image into a feature image. We then extract the most discriminate features from the feature image by using the block-based center-symmetric local binary pattern. Finally, these features are classified by the support vector machine. Experimental results are provided to illustrate the proposed approach is an effective method, compared to other similar methods.     

光学小波包变换及其滤波器的研究

基于对光学小波变换必要条件的分析,提出光学小波包变换的概念.选出虹膜图库的联合最优小波包基,利用最优基的线性组合生成相应的复合光学小波包滤波器.将滤波器用于光电混合虹膜识别系统中对待识别输入进行小波包特征提取预处理,模拟结果不仅证明引入该滤波器可明显提升系统的识别效果,也证明了光学小波包变换提出的意义.     

基于高光谱成像技术的番茄叶片灰霉病早期检测研究

提出了独立软模式法(SIMCA)的番茄叶片灰霉病特征波段图像的提取,并通过多元线性回归法(MLR)提取波段融合图像,通过最小距离法获取番茄灰霉病患病信息的技术路线。利用680～740 nm波段的方差图像和建模能力参数提取的特征波段,并作为输入变量进行MLR分析,在0.5准确率阈值下,准确率均大于99%,说明特征波段可以实现番茄叶片灰霉病的检测,并利用MLR回归系数提取波段融合图像,通过最小距离法获取番茄灰霉病患病信息,结果表明所提出的方法具有很好的预测能力,为番茄灰霉病的早期检测提供了一种新方法,且大大降低了高光谱图像的数据处理时间。