Eye Detection in Facial Images Using Zernike Moments with SVM

An eye detection method for facial images using Zernike moments with a support vector machine (SVM) is proposed. Eye/non‐eye patterns are represented in terms of the magnitude of Zernike moments and then classified by the SVM. Due to the rotation‐invariant characteristics of the magnitude of Zernike moments, the method is robust against rotation, which is demonstrated using rotated images from the ORL database. Experiments with TV drama videos showed that the proposed method achieved a 94.6% detection rate, which is a higher performance level than that achievable by the method that uses gray values with an SVM.     

Real-time palmprint acquisition system design

Capturing high quality palmprint images in a very short time period is a fundamental step in palmprint research. While a palmprint scanner capable of this function is especially important for developing a real-time online palmprint identification system, limited research effort has been put into designing and implementing palmprint acquisition systems. A novel palmprint acquisition system is presented, which is designed for various civilian applications such as access control, ATM, etc. The novel contactless design on the user interface prevents the distortion of the palm skin introduced by the traditional glass plate of a palm scanner. The system is designed to have the best performance at reasonable price. It has a resolution of 150 dpi that is able to obtain palmprint features, including the principal lines, wrinkles and ridge texture. The proposed system provides an important step for implementing a personal identification system using palmprint. Over 9,400 palmprint images were collected by the proposed system. An online palmprint identification system was then developed based on the proposed acquisition system.     

基于局部Zernike矩的RST不变水印

旋转、缩放和平移(RST)等几何攻击能够破坏水印检测的同步性,而使常规水印检测失败,提出了一种基于图像局部Zernike矩的RST不变水印算法.利用图像归一化后的Zernike矩幅度具有RST不变的性质,将由Zernike矩重构的水印图像在空域嵌入原始图像的局部中,并提取该区域的Zernike矩幅度矢量作为水印矢量.水印检测时,计算局部区域的Zernike矩,并与已知Zernike矩矢量计算均方误差根(RMSE)判断水印存在与否.与使用图像全局Zernike矩相比,使用局部矩使得水印具有更好的旋转不变性,同时水印对于缩放、JPEG压缩和噪声等攻击也具有较好的检测性能.     

Exact Zernike and pseudo-Zernike moments image reconstruction based on circular overlapping blocks and Chamfer distance

This study aims to explore a novel approach to reconstruct multi-gray-level images based on circular blocks reconstruction method using two exact and fast moments: Zernike (CBR-EZM) and pseudo-Zernike (CBR-EPZM): An image is first divided into a set of sub-images which are then reconstructed independently. We also introduced Chamfer distance (CD) to capitalize on the use of discrete distance instead of Euclidean one. The combination of our methods and CD leads to CBR-EZM-CD and CBR-EPZM-CD methods. Obviously, image partitioning offers significant advantages, but an undesirable circular blocking effect can occur. To mitigate this effect, we have implemented overlapping feature to our new methods leading to OCBR-EZM-CD and OCBR-EPZM-CD, by exploiting neighborhood information of the circular blocks. The main motivation of this novel approach is to explore new applications of Zernike and pseudo-Zernike moments. One of the fields is feature extraction for pattern recognition: Zernike and pseudo-Zernike moments are well known to capture only the global features, but thanks to the circular block reconstruction, we can now use those moments to extract also local features.     

基于等积环投影与Zernike矩的快速模板匹配

为增强工业级机器视觉系统的实时性和稳定性,本文提出一种基于等积环投影与Zernike矩的具有旋转不变性的快速模板匹配方法,该方法采用由粗到精匹配策略,在粗匹配阶段以等积环投影向量作为特征进行匹配得到候选点集,再基于Zenrike矩进行精确匹配.主要创新点为提出复杂度低、抗噪性强和具有旋转不变性的等积环投影向量特征,并利用圆对称性在八分圆法基础上进一步降低Zernike矩计算量.经理论分析和实验对比验证,此法在保障匹配精度前提下,其匹配速度胜于当前同类最优算法,且对高斯噪声及线性亮度变化有强鲁棒性.     

基于最近相关性分类器的单样本掌纹识别

为了解决单样本掌纹识别的困难,研究了基于最近相关性分类器(NCC)的单样本掌纹识别方法。首先对掌纹图像进行分块,划分为若干个子图像;然后运用统计特征、傅里叶变换、离散余弦变换(DCT)和Gabor变换4种方法对子图像进行特征提取,将所有子图像的特征向量组合在一起形成该图像的特征向量;最后应用NCC进行分类识别。运用PolyU掌纹图像库,对本文算法进行测试。实验结果表明:与最近邻分类器(NNC)和支持向量机(SVM)相比,在不同大小的子图像上,运用不同的特征提取算法,NCC均提高了识别率;分类时间在0.3～0.7s之间,满足实时系统的需求。     

Robust image watermarking using local Zernike moments

In this work, we propose a robust image watermarking algorithm using local Zernike moments, which are computed over circular patches around feature points. The proposed algorithm locally computes Zernike moments and modifies them to embed watermarks, achieving robustness against cropping and local geometric attacks. Moreover, to deal with scaling attacks, the proposed algorithm extracts salient region parameters, which consist of an invariant centroid and a salient scale, and transmits them to the decoder. The parameters are used at the decoder to normalize a suspect image and detect watermarks. Extensive simulation results show that the proposed algorithm detects watermarks with low error rates, even if watermarked images are distorted by various geometric attacks as well as signal processing attacks.     

基于小波变换和部分最小二乘的掌纹识别

子空间法作为一种传统的识别方法,识别时基于整幅图像,复杂性比较高,而且没有考虑类别信息.为了降低计算复杂性和在提取数据特征的同时融入类别信息,研究了一种基于小波变换和部分最小二乘(PLS)的掌纹识别算法.在建议的识别方法中,首先通过小波三级分解提取低频子图像,对低频子图像应用PLS提取掌纹特征,然后将样本投影到提取的特征上作为特征向量进行分类识别.应用PolyU掌纹图像库进行实验分析,实验结果表明:与主元分析(PCA)、二维主元分析(2DPCA)和独立主元分析(ICA)相比,该方法的识别率得到了很大的提高,大大减小了误识率和拒识率,验证了该方法的有效性.     

Personal recognition using hand shape and texture.

This paper proposes a new bimodal biometric system using feature-level fusion of hand shape and palm texture. The proposed combination is of significance since both the palmprint and hand-shape images are proposed to be extracted from the single hand image acquired from a digital camera. Several new hand-shape features that can be used to represent the hand shape and improve the performance are investigated. The new approach for palmprint recognition using discrete cosine transform coefficients, which can be directly obtained from the camera hardware, is demonstrated. None of the prior work on hand-shape or palmprint recognition has given any attention on the critical issue of feature selection. Our experimental results demonstrate that while majority of palmprint or hand-shape features are useful in predicting the subjects identity, only a small subset of these features are necessary in practice for building an accurate model for identification. The comparison and combination of proposed features is evaluated on the diverse classification schemes; naive Bayes (normal, estimated, multinomial), decision trees (C4.5, LMT), k-NN, SVM, and FFN. Although more work remains to be done, our results to date indicate that the combination of selected hand-shape and palmprint features constitutes a promising addition to the biometrics-based personal recognition systems.     

基于Blob-Harris特征区域和NSCT-Zernike的鲁棒水印算法

为了有效抵抗水印图像的几何攻击,该文提出了一种基于Blob-Harris特征区域和非下采样轮廓波变换(NSCT)和伪Zernike矩的鲁棒水印算法。首先原始图像进行两层非下采样Contourlet变换后提取其低频图像,然后利用Blob-Harris检测算子对低频图像进行特征点提取,根据各个特征点的特征尺度确定其特征区域,优化筛选出稳定且互不重叠的特征区域并将其四周补零,得到稳定的互不重叠的方形特征区域作为水印嵌入区域,最后计算每一个方形特征区域的Zernike矩,将水印信息嵌入在量化调制正则化Zernike矩的幅值当中。实验结果表明,Lena图峰值信噪比达到40 dB以上时,本文算法对常规图像处理以及缩放、旋转、剪切等几何攻击和组合攻击都有相对较强的鲁棒性。     

基于Zernike矩的低信噪比红外目标检测方法

针对低信噪比红外目标图像,分析了Zernike矩的基本原理、计算方法和旋转不变性,提出了基于Zernike正交矩的低信噪比红外目标检测方法,并比较了模板匹配、Hu矩、Zernike矩方法的目标识别效果.理论分析与实验验证了所提方法的有效性.     

A new Zernike moments based technique for camera identification and forgery detection

In multimedia forensics, it is important to identify those images that were captured by a specific camera from a given set of N data images as well as detecting the tampered region in these images if forged. This paper presents a new technique based on Zernike moments feature extraction for blindly classifying correlated PRNU images as well as locating the tampered regions in image under investigation. The proposed clustering algorithm is based on estimating the Zernike moments and applying a hierarchical clustering for classification. The forgery detection algorithm is based on picking up the peak Euclidean distance between the Zernike moments vector of blocks of the scaled-down forged image and its corresponding ones in the capturing camera PRNU. As Zernike moments are scale and rotational invariant, its feature when computed using scaled-down PRNU images lead to considerable computation time saving. Simulation examples are given to verify the effectiveness of the proposed techniques when compared to other state-of-the-art techniques even in case of very weakly correlated PRNU.     

基于InSAR的三维地形匹配导航技术的研究与实现

研究了一种以干涉合成孔径雷达(InSAR)信息为基础的三维地形匹配导航系统,该系统采用基于3-D Zernike矩的三维地形匹配算法,同时针对3-D Zernike矩在地形匹配中计算实时性差的问题进行了改进。为验证系统的有效性和算法性能,搭建了基于VC++和OpenSceneGraph的三维可视化软件仿真平台。仿真结果表明,基于3-D Zernike矩的三维地形匹配算法定位精确度高,对地形的适应能力强,算法的实时性问题得到了良好解决,系统具有较高的工程实用价值。     

一种基于Zemike矩和稳态遗传算法的遥感图像匹配方法

该文提出了一种基于稳态遗传算法和Zernike矩的遥感图像匹配方法。将稳态遗传算法快速而有效的全局寻优特点应用于遥感图像的匹配定位。考虑到旋转畸变,采用遥感图像的Zernike矩作为相似性量度,实现遥感图像的匹配。实验结果表明,该算法可以克服图像的旋转畸变,在保证一定的匹配精度下,具有较高的匹配效率。     

基于Harris特征点和伪Zernike矩的鲁棒水印算法

抵抗几何攻击的鲁棒性是目前数字水印技术中的热点亦是难点。文中以Harris检测算子和伪Zernike矩为理论基础,提出一种有效抵抗几何攻击的鲁棒水印算法。通过提取归一化图像的Harris特征点,选取部分稳定特征点确定圆形区域并计算伪Zernike矩,并量化调制伪Zernike矩的幅值来嵌入水印信息。实验结果表明,本算法对于抗几何攻击具有较好的鲁棒性,同时也能抵抗常规的信号处理攻击。     

Gabor-based kernel PCA for palmprint recognition

A novel Gabor-based kernel principal component analysis (PCA) method by integrating the Gabor wavelet representation of palm images and the kernel PCA method for palmprint recognition is proposed. The feasibility of the proposed method has been successfully tested on two different public data sets from the PolyU palmprint databases, for which the samples were collected in two different sessions.