Palmprint recognition based on the representation in the feature space

In this paper, we propose a palmprint recognition method based on the representation in the feature space. The proposed method seeks to represent the test sample as a linear combination of all the training samples in the feature space and then exploits the obtained linear combination to perform palmprint recognition. We can implement the mapping from the original space to the feature space by using the kernel functions such as radial basis function (RBF). In this method, the selection of the parameter of the kernel function is important. We propose an automatic algorithm for selecting the parameter. The basic idea of the algorithm is to optimize the feature space such that the samples from the same class are well clustered while the samples from different classes are pushed far away. The proposed criterion measures the goodness of a feature space, and the optimal kernel parameter is obtained by minimizing this criterion. Experimental results on multispectral palmprint database show that the proposed method is more effective than 2DPCA, 2DLDA, AANNC, CRC_RLS, nearest neighbor method (NN) and competitive coding method in terms of the correct recognition rate.     

用于特征提取的Gabor滤波器参数设计

Gabor滤波器的参数设计是Gabor特征提取过程中的一个重要环节。根据坦克的外形特,最及对方向的敏感性对其进行了针对性的参数设置,实验结果证明得到了很好的滤波效果。根据Gabor核函数的窗口特性,提出了一种自适应的基于Gabor滤波器的特征提取算法。该算法应用Gabor滤波器的多尺度特性与样本图像进行卷积,对Gabor域响应进行局部极大值提取,经过该方法得到的特征点有效地减少了数据冗余,并且具有较好的图像表征能力。     

基于多光谱图像融合的掌纹识别方法

生物特征识别在信息安全领域发挥着重要作用,掌纹识别作为一种新型生物特征识别方式,具有低失真、非侵入性和高唯一性等优势。传统掌纹研究大多使用自然光成像系统以灰度格式获取,识别精度很难进一步提升。为了获得更多的身份鉴别信息,提出利用多光谱掌纹图像代替自然光掌纹图像。针对现有掌纹识别算法由于没有考虑到不同光谱的特性而导致纹理细节丢失,识别精准率低的问题,提出了一种基于多光谱图像融合的掌纹识别算法。该方法通过对不同光谱下的掌纹图像进行快速自适应二维经验模式分解（FABEMD）,将多光谱掌纹图像分解成一系列频率由高到低的二维固有模态函数(BIMF)和一个残余分量,残余分量可被视为该光谱图像低频信息的初步估计。图像采集过程中光照条件很难保持稳定,而近红外光谱图像在进行FABEMD分解时对光照变换敏感,容易导致分解后的BIMF背景信息过于冗余;因此对分解后的近红外掌纹图像进行背景重建及特征细化,在对背景冗余信息进行平滑处理的同时可以有效增强高频信息的特征表达。为避免直接融合处理后引发的图像过度曝光问题,提出对近红外特征压缩后再融合。此外,提出了一种结合了注意力机制的改进残差网络（IRCANet）,用于融合后的掌纹图像分类,在网络中引入分阶段残差结构,缓解了网络的退化问题,在学习过程中有效地减少信息丢失,对于融合后的多光谱掌纹图像,分阶段残差结构能够稳定地将图像信息在网络间传输,但对图像中的高低频信息区分效果不够显著,为了使网络关注更多区分性特征,利用特征通道间的相互依赖性,在分阶段残差结构中结合了通道注意力(Channel Attention)机制。最终,在香港理工大学（PolyU）多光谱掌纹数据集上进行的综合实验表明,该方法可以取得良好的效果,算法识别准确率能达到99.67%且具有良好的实时性。     

Fractal structure of digital speckle patterns produced by rough surfaces

We report on the fractal analysis of digital speckle patterns experimentally generated using an optical setup to record the light scattered from metallic rough surfaces in the normal direction. Using the differential box counting technique, we have calculated the fractal dimension of digital speckle patterns for six samples with different roughness. Our results show a quadratic dependence between the surface roughness and the fractal dimension of the corresponding digital speckle pattern. As an application a method to determine the surface roughness of metallic surfaces is proposed.     

一种自适应的Gabor图像特征抽取和权重选择的人脸识别方法

 为了克服光照、表情变化等因素对人脸识别的影响,本文提出了一种自适应的Gabor图像特征抽取和权重选择的人脸识别方法.该方法首先把每幅人脸图像经过Gabor小波变换后得到的40个不同尺度和方向下的图像都看作是独立的样本,再把不同人脸中的同一尺度和方向的变换结果进行特征重组,得到40个独立地新特征矩阵.为了增强对光照、表情变化的鲁棒性,每一新特征矩阵的识别贡献被本文所提出的自适应权重方法计算得到.其次,对每一新特征矩阵采用离散余弦变化进行降维,并采用了鉴别力量分析方法来选取最有鉴别力的离散余弦变换系数作为特征向量.最后,抽取线性鉴别分析特征进行识别.大量的实验证明了本文所提方法的有效性.     

Feature fusion of palmprint and face via tensor analysis and curvelet transform

In order to improve the recognition accuracy of the unimodal biometric system and to address the problem of the small samples recognition, a multimodal biometric recognition approach based on feature fusion level and curve tensor is proposed in this paper. The curve tensor approach is an extension of the tensor analysis method based on curvelet coefficients space. We use two kinds of biometrics: palmprint recognition and face recognition. All image features are extracted by using the curve tensor algorithm and then the normalized features are combined at the feature fusion level by using several fusion strategies. The k-nearest neighbour (KNN) classifier is used to determine the final biometric classification. The experimental results demonstrate that the proposed approach outperforms the unimodal solution and the proposed nearly Gaussian fusion (NGF) strategy has a better performance than other fusion rules.     

Local and global Gabor features for raised character recognition

Conventional Gabor representation and its exttacted features often yield a fairly poor performance in extracting the invariance features of objects.To address this issue,a global Gabor representation method for raised characters pressed on label is proposed in this paper,where the representation only requires few summations on the conventional Gabor filter responses.Features are then extracted from these new representations to construct the invariant features.Experimental results clearly show that the obtained global Gabor feattires provide good performance in rotation,translation,and scale invariance.Also,they are insensitive to illumination conditions and noise changes.It is proved that Gabor filters can be reliably used in olw-level feature extraction in image processing and the global Gabor features can be used to construct robust invariant recognition system.     

Ship recognition via its radiated sound: the fractal based approaches

Due to the complexity of its radiated sound, ship recognition is difficult. Fractal approaches are proposed in this study, including fractal Brownian motion based analysis, fractal dimension analysis, and wavelet analysis, to augment existing feature extraction methods that are based on spectrum analysis. Experimental results show that fractal approaches are effective. When used to augment two traditional features, line and average spectra, fractal approaches led to better classification results. This implies that fractal approaches can capture some information not detected by traditional approaches alone.     

基于多通道Gabor滤波器的高鲁棒灰度图像目标识别新方法

提出了一种针对低质量的灰度图像的基于多通道Gabor小波滤波器的高鲁棒目标识别新方法。主要是利用Gabor小波设计了滤波器,滤波器的中心频率是一个从低到高的范围。滤波器采用不同方向、不同尺度,从而组成多通道滤波器。对灰度图像直接进行小波变换,用Gabor小波变换系数的模的平均值和其标准方差来表示抽取的灰度图像目标的特征,并对获得的小波特征归一化后输入到改进的BP神经网络分类器中进行分类识别。对四种不同的飞机灰度图像目标进行了分类识别仿真实验。结果表明,这种特征提取方法能有效地提取灰度图像目标纹理特征,并且对噪音和形状的变化具有强鲁棒性。在应用灰度图像对目标进行识别时,神经网络的训练时间减少到10min,识别率达到94%。     

基于Gabor小波纹理特征的目标识别新方法

给出了一种基于Gabor小波纹理特征的目标识别新方法．主要是利用Gabor小波设计了一种多通道小波滤波器。对图像目标直接进行小波变换，用Gabor小波变换系数的模的平均值和其标准方差来表示抽取的图像目标的特征，把获得的小波特征归一化后输入到改进的BP神经网络分类器进行分类识别．最后。进行了一系列的仿真实验，结果表明，这种特征提取方法能有效提取图像目标纹理特征，并且对噪音和形状的变化具有鲁棒性．在应用于目标识别时，神经网络的训练时间减少到lOmin，识别率达到94％．     

油气水多相流压力和压差信号特征分析与流型在线识别

在宽广的实验参数范围内测量了水平管内油气水多相流动时压力和压差信号，对信号的时域、频域、小波尺度域、分形等特征进行了提取与分析；建立了流型的规则识别和模式识别的融合方法。经过实验测试，该方法可以识别出泡状流、分层流、间歇流和环状流，流型识别率高于９０％．     

基于Gabor小波变换的ICA火灾图像纹理识别算法

针对火灾图像纹理识别问题,提出了基于Gabor小波变换的ICA火灾图像纹理识别算法,并根据火灾图像纹理识别特点进行了优化。首先用不同尺度和方向的Gabor滤波器对待识别图像滤波,得到其特征图像,然后将特征图像转化成特征向量作为ICA的输入,得到基矢量子空间,再将测试图像经过Gabor滤波器的特征向量投影到ICA子空间中得到系数向量作为目标识别特征,最后用支持向量机进行识别。通过与Gabor滤波器法和ICA方法的对比实验,表明该算法可以在火灾纹理图像的识别率上比传统方法提高5%以上,为火灾图像识别提供了一种新思路。     

空温式翅片管气化器结霜模型及数值模拟

以分形理论的DLA模型为基础,建立了空温式翅片管气化器深冷表面上霜晶生长的二维模型,模拟了深冷表面上的霜晶生长过程.采用计盒维数法对模拟出的霜晶生长图像进行了分形维数的计算,结果表明深冷表面上霜晶的分形维数较一般冷表面上的分形维数大,从而说明深冷表面上的霜晶具有更加复杂的结构,充满空间的能力更大.这对进一步理解空温式深...     

一种抗姿态与表情变化的三维人脸识别方法

为了提高人脸在姿态和表情变化下的识别率,结合局部平面距离(DLP)对曲面局部凹凸性优良的判断能力,提出了一种采用人脸的等距不变表示形式来匹配的人脸识别方法。首先,对深度摄像头采集到的深度图像进行距离约束、位置约束、转换等操作,得到干净完整的三维人脸,利用三维人脸上每一点DLP值确定鼻尖点,利用聚类的思想确定鼻根点;其次,采用改进的快速推进算法计算人脸的测地距矩阵,设置阈值并切割出有效的人脸区域;最后,计算有效的人脸区域的高阶矩特征,作为人脸的特征向量进行匹配。实验结果表明,对于不同的数据库,本文算法的识别率接近97%;将本文算法与基于轮廓线特征的人脸识别算法以及基于Gabor特征的人脸识别算法进行比较,其识别率分别提高了14.1%和8.3%,同时有着较高的运算效率。     

多重分形在掌纹识别中的研究

通过对掌纹的概率密度分布和配分函数的分析，得到掌纹分布具有一定的多重分形性.进一步求取掌纹多重分形谱的宽度、极大值以及谱曲线的不对称程度，并提出以这些参数作为掌纹识别的特征量.这可能为多重分形理论在生物特征识别领域中的应用带来新的思路与方法. 关键词： 分形 模式识别     

基于FRFT域分形特征的压制干扰存在性检测（英）

基于分数阶傅里叶变换（FRFT）域的分形特征研究压制干扰的存在性检测问题。首先,分析了典型的三种压制干扰的分形特征,说明了典型压制干扰信号具有分形特性,并采用盒维数和信息维数定量描述它们的分形特征。然后,发现了压制干扰和高斯白噪声在FRFT域具有不同的分形特征,进而提出了一种检测压制干扰存在性的方法。最后,仿真验证了该方法的有效性和优越性。     

Palmprint recognition method based on score level fusion

Different palmprint recognition methods have different advantages. The texture- and feature-based palmprint recognition methods can well exploit the minutiae of the palmprint but are not very robust to the possible variation such as the rotation and shift of the palm. The representation-based palmprint recognition method can well take advantage of the holistic information but seems not to be able to fully exploit the minutiae of the palmprint. In this paper, we propose to fuse the competitive coding method and two-phase test sample sparse representation (TPTSR) method for palmprint recognition. As one of representation-based methods, TPTSR method takes the whole palmprint image as the input and determines the contribution of the training samples of each class in representing the test sample. TPTSR also uses the contribution to calculate the similarities between the test sample and every class. The competitive coding method is a feature-based method and is highly complementary with TPTSR. We use a weighted fusion scheme to combine the matching scores generated from TPTSR and the competitive coding method. The experimental results show that the proposed method can obtain a very high classification accuracy and outperforms both TPTSR and the competitive coding method.     

Facial expression recognition algorithm using LGC based on horizontal and diagonal prior principle

The traditional Local Binary Pattern (LBP) algorithm can analyze the center pixel and neighboring pixels of the gray relationship, using in facial expression recognition, but you cannot consider the eyes, mouth, forehead and other areas in the expression feature different trends in the gradient direction. Firstly, we propose the Local Gradient Coding (LGC) algorithm, though the binary encoding to the horizontal, vertical and diagonal gradients respectively, to produce the fusion characteristic, then this can fully describe the facial muscles texture, wrinkles and other local deformation of contains the expression information. On the other hand, in order to reduce the computational complexity, and to remove the redundant, while not lose the main information contained in the face texture expression. This paper proposes and optimizes a new LGC operator based on horizontal and diagonal gradient prior principle (LGC-HD). The experimental results from JAFFE database show that, LGC-HD algorithm is more quickly and effectively to extract facial expression feature than LGC algorithm. Comparing to the traditional LBP algorithm, LBP uniform pattern and Gabor filtering, this LGC-HD algorithm has a significant advantage in the recognition accuracy and run time.     

新生儿疼痛面部表情识别方法的研究

针对新生儿的疼痛与非疼痛面部表情识别,提出将Gabor变换和支持向量机(SVM)相结合的分类识别方法.对归一化后的大小为112 pixel×92 pixel的新生儿面部图像进行二维Gabor小波变换,提取出412160维Gabor特征;针对Gabor特征向量维数高、冗余大的特点,采用Adaboost算法作为特征选择工具,去除冗余的Gabor特征,从412160维特征中选取出900维Gabor特征;对选取出的Gabor特征用SVM进行疼痛表情的分类识别.该方法综合运用Gabor特征对于面部表情的良好表征能力、AdaBoost算法的特征选择能力以及SVM在处理少样本、高维数问题中的优势.对510幅新生儿的表情图像进行测试的结果表明,疼痛与非疼痛表情的分类识别率达到85.29%,疼痛与安静表情的分类识别率达到94.24%,疼痛与哭表情的分类识别率达到78.24%.     

面向结构网格自适应并行计算的矩形区域求差集快速算法

结构网格自适应程序需要使用矩形区域求差集算法计算网格层间数据依赖关系和网格层嵌套关系.原有的矩形区域求差集算法时间复杂度较高,成为该类应用大规模并行计算可扩展性能瓶颈.本文利用分而治之的方法,构造近似线性时间复杂度的矩形区域求差集快速算法,并利用区域分解实现该算法的并行计算.分别针对规则矩形区域和多层自适应网格的非规则矩形区域求差集问题,验证该算法的效率.结果表明,该算法具有近似线性计算复杂度,对于大规模计算问题,加速效果显著.