Finger crease pattern recognition using Legendre moments and principal component analysis

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper,we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the preprocessing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.     

Face recognition based on PCA and logistic regression analysis

Face recognition is an important research hotspot. More and more new methods have been proposed in recent years. In this paper, we propose a novel face recognition method which is based on PCA and logistic regression. PCA is one of the most important methods in pattern recognition. Therefore, in our method, PCA is used to extract feature and reduce the dimensions of process data. Afterwards, we present a novel classification algorithm and use logistic regression as the classifier for face recognition. The experimental results on two different face databases are presented to illustrate the efficacy of our proposed method.     

基于子空间分析的人脸识别方法研究

人脸识别技术是模式识别和机器视觉领域的一个重要研究方向,在众多人脸识别的算法中,基于子空间分析的特征提取方法以其稳定可靠的识别效果成为了人脸识别中特征提取的主流方法之一。本文对目前应用较多的子空间分析方法进行了研究,具体介绍了线性子空间分析方法：主成分分析（PCA）、线性鉴别分析（LDA）、独立主成分分析（ICA）、快速主成分分析（FastICA）等及非线性子空间分析方法：基于核的PCA （KPCA）等的基本思想及其在人脸识别中的研究进展,包括一些新的研究成果。此外,还应用orl及Yale B人脸库对几个基础的子空间方法进行了验证实验。实验结果表明,在几个子空间分析方法中,FastICA算法取得了最高的识别率。最后结合实验结果对各算法的优缺点进行了分析总结。     

Multi-radius centralized binary pattern histogram projection for face recognition

The existing local binary pattern (LBP) operators have several disadvantages such as rather long histograms,lower discrimination,and sensitivity to noise.Aiming at these problems,we propose the centralized binary pattern (CBP) operator.CBP operator can significantly rcduce the histograms' dimensionality,offer stronger discrimination,and decrease the white noise's influence on face images.Moreover,for increasing the recognition accuracy and speed,we use multi-radius CBP histogram as face representation and project it onto locality preserving projection (LPP) space to obtain lower dimensional features.Experiments on FERET and CAS-PEAL databases demonstrate that the proposed method is superior to other modern approaches not only in recognition accuracy but also in recognition speed.     

Enhanced computational integral imaging system for partially occluded 3D objects using occlusion removal technique and recursive PCA reconstruction

In this paper, we propose an enhanced computational integral imaging system by both eliminating the occlusion in the elemental images recorded from the partially occluded 3D object and recovering the entire elemental images of the 3D object. In the proposed system, we first obtain the elemental images for partially occluded object using computational integral imaging system and it is transformed to sub-images. Then we eliminate the occlusion within the sub-images by use of an occlusion removal technique. To compensate the removed part from occlusion-removed sub-images, we use a recursive application of PCA reconstruction and error compensation. Finally, we generate the entire elemental images without a loss from the newly reconstructed sub-images and perform the process of object recognition. To show the usefulness of the proposed system, we carry out the computational experiments for face recognition and its results are presented. Our experimental results show that the proposed system might improve the recognition performance dramatically.     

Nonparametric subspace analysis fused to 2DPCA for face recognition

Two-dimensional principal component analysis (2DPCA) is one of the representative techniques for image representation and recognition. However, keen storage requirements and computational complexity consist in 2DPCA. Meanwhile, the performance of 2DPCA is delicate in illumination variations. Nonparametric subspace analysis (NSA) is a subspace learning method that can reduce dimensionality and identify local information for discrimination, so that it can make 2DPCA perform well in illumination. Motivated by above facts, 2DPCA fused with NSA is implemented for face recognition, which can reduce dimensions of the 2DPCA feature vectors and enhance the contribution of principal components to face recognition. Experiments carried out on ORL, Yale B, and FERET facial databases show that valid recognition rates can be achieved by the proposed method compared to 2DPCA, 2DPCA plus PCA, LDA methods and demonstrate promising abilities against illumination variations.     

基于遗传算法与线性鉴别的近红外光谱玉米品种鉴别研究

结合遗传算法与线性签别分析(LDA)提出了一种玉米品种的快速鉴别方法.该方法是一种基于近红外光谱的新方法,通过采集玉米种子(实验共37个种类)的近红外光谱数据,使用遗传算法进行特征光谱波段的选择,使用线性鉴别分析的方法提取光谱特征并分类.结果表明,遗传算法能有效地剔除光谱噪声波段,并提高 LDA 的泛化能力.同时,为简化运算,剔除了大量冗余数据,结合遗传算法选择的特征谱区,使参与鉴别的数据维数从2 075降到了233.对测试集1的300个样本的平均正确识别率与平均正确拒识率均达到99.30%,其中73.33%的玉米品种的正确识别率达到了100%;对测试集2(均为未参加训练品种的样本)的175个样本的平均正确拒识率达到99.65%.与常用的 PCA 等方法相比,运算时间更短,正确率更高.     

Fingerprint image enhancement using data driven Directional Filter Bank

A new method based upon data driven tool, principal component analysis (PCA), for fingerprint enhancement is proposed in this paper. PCA is a very useful statistical technique that has found application in many different fields like image compression, face recognition and is commonly used for finding patterns in data of high dimension. In the proposed method, the input image is first decomposed into directional images using decimation free Directional Filter Bank (DDFB). Then these directional images are normalized. A data driven technique PCA is applied to these normalized directional fingerprint images, which gives the PCA filtered images. These are basically directional images. Then these directional images are reconstructed into one image which is the enhanced one. Simulation results are included illustrating the capability of the proposed method.     

Producing virtual face images for single sample face recognition

For single sample face recognition, there are limited training samples, so the traditional face recognition methods are not applicable to this problem. In this paper we propose to combine two methods to produce virtual face images for single sample face recognition. We firstly use a symmetry transform to produce symmetrical face images. We secondly use the linear combination of two samples to generate virtual samples. As a result, we convert the special single sample problem into a non-single sample problem. We then use the 2DPCA method to extract features from the samples and use the nearest neighbor classifier to perform classification. Experimental results show that the proposed method can effectively improve the recognition rate of single sample face recognition.     

Kernel-aligned multi-view canonical correlation analysis for image recognition

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.     

基于NIR分析和模式识别技术的玉米种子识别系统

模式识别技术及数据挖掘方法已成为化学计量学的研究热点。近红外(NIR)光谱分析以其快速、简便、非破坏性等优势广泛应用于光谱信号的处理和分析模型的建立。文章基于五种不同的模式识别方法:局部线性嵌入(LLE),小波变换(WT),主成分分析(PCA),偏最小二乘(PLS)和支持向量机(SVM),利用NIR技术建立了玉米种子的模式识别系统,并将其应用于108玉米杂交种和母本178种子的近红外光谱样品。首先利用LLE,WT,PCA,PLS进行消噪或降维,然后运用SVM进行分类识别,而一模支持向量机(1-norm SVM)算法直接进行分类识别。三个不同NIR光谱范围的数值实验显示:PCA+SVM,LLE+SVM,PLS+SVM识别效果甚佳,而WT+SVM和1-norm SVM方法也有较高的分类精度。实验结果表明了本文提出方法的可行性和有效性,为利用近红外光谱和模式识别技术进行种子识别研究提供了理论依据和实用方法。     

基于NIR分析和模式识别技术的玉米种子识别系统

模式识别技术及数据挖掘方法已成为化学计量学的研究热点。近红外(NIR)光谱分析以其快速、简便、非破坏性等优势广泛应用于光谱信号的处理和分析模型的建立。基于五种不同的模式识别方法:局部线性嵌入(LLE),小波变换(WT),主成分分析(PCA),偏最小二乘(PLS)和支持向量机(SVM),利用NIR技术建立了玉米种子的模式识别系统,并将其应用于108玉米杂交种和母本178种子的近红外光谱样品。首先利用LLE,WT,PCA,PLS进行消噪或降维,然后运用SVM进行分类识别,而一模支持向量机(1-normSVM)算法直接进行分类识别。三个不同NIR光谱范围的数值实验显示:PCA+SVM,LLE+SVM和PLS+SVM识别效果甚佳,而WT+SVM和1-norm SVM方法也有较高的分类精度。实验结果表明了本文提出方法的可行性和有效性,为利用近红外光谱和模式识别技术进行种子识别研究提供了理论依据和实用方法。     

改进的基于二维主分量分析的掌纹识别

主分量分析(PCA)是一种在众多生物特征识别中获得成功应用的特征提取技术,是一种基于二阶统计的在最小均方误差意义上的最优维数据压缩技术,它所提取的各特征分量之间是互不相关的。传统的PCA变换是对图像向量的分析,但向量维数一般都很高。二维主分量分析方法是最近兴起的针对图像矩阵的主分量分析方法,与一维主分量分析相比能更精确的计算原始数据的协方差矩阵。将其应用于掌纹识别,并在主分量的选取上加以改进,选取了更适合于分类的主分量。实验结果表明,该方法不仅有更高的识别率,而且维数更低。     

基于LIBS与化学计量学的植物叶片分类研究

激光诱导击穿光谱（LIBS）是一种高效快速的光谱采集手段,可应用于各类物质的元素分析工作中。线性判别分析（LDA）与支持向量机（SVM）是化学计量学中两种常用的有监督算法,均通过对已知不同种类的样本数据进行学习建模,进而实现对未知类别数据的归类。为了实现LIBS技术对有机物的高准确率识别,将这两种算法应用到LIBS光谱数据的分类中。实验利用波长为1 064 nm的纳秒激光烧蚀女贞、珊瑚树、竹子三种植物的叶片,并采集每种树叶220～432 nm波段的100组光谱数据。通过对300组样本的原始光谱数据进行主成分提取,由第一主成分（PC1）和第二主成分（PC2）的得分图得出三种植物光谱的相似度非常高。然后,利用每种叶片70组样本的光谱数据作为训练集建模,其余30组光谱数据作为测试集来进行树叶种类的预测识别。将PCA对原始光谱数据提取得到的前20个主成分作为LDA与SVM建模的属性值。对于LDA算法,将属性值分析后得到前两个判别函数值,通过聚类分析发现不同种类的植物叶片光谱数据在空间上的分离效果较好,同一种类基本聚集在一起。再借助马氏距离可得到测试集的平均分类正确率为96.67%。与此类似,使用SVM方法对训练集样本的数据进行学习得到分类超平面,对测试集的平均分类正确率达到98.9%。研究结果表明,经过PCA对数据的预处理,再结合LDA,SVM这两种方法可实现LIBS技术应用于复杂有机物的快速准确分类,并且PCA与SVM结合的分类正确率更高。该方法可在食品快速溯源、生物组织原位鉴别、有机爆炸物远程分析等领域应用。     

Application of the new pattern recognition system in the new e-nose to detecting Chinese spirits

We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits.     

A two-phase face recognition method in frequency domain

In this paper, we propose a two-phase face recognition method in frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The absolute values of DCT coefficients or DFT amplitude spectra are used to represent the face image, i.e. the transformed image. Then a two-phase face classification method is applied to the transformed images. This method is as follows: its first phase uses the Euclidean distance formula to calculate the distance between a test sample and each sample in the training sets, and then exploits the Euclidean distance of each training sample to determine K nearest neighbors for the test sample. Its second phase represents the test sample as a linear combination of the determined K nearest neighbors and uses the representation result to perform classification. In addition, we use various numbers of DCT coefficients and DFT amplitude spectra to test the effect on our algorithms. The experimental results show that our method outperforms the two-phase face recognition method based on space domain of face images.     

‘Symmetrical face’ based improved LPP method for face recognition

As one of the most important branches of pattern recognition and computer vision, face recognition has more and more become the focus of researches. In real word applications, the face image might have various changes owing to varying illumination, facial expression and poses, so we need sufficient training samples to convey these possible changes. However, most face recognition systems cannot capture many face images of every user for training, non-sufficient training samples have become one bottleneck of face recognition. In this paper, we propose to exploit the symmetry of the face to generate ‘symmetrical face’ samples and use an improved LPP method to perform classification. Experimental results show that our method can get a high accuracy.     

A real time implementation of a pose invariant face recognition engine with a novel face segmentation algorithm

In this paper we propose a fast and efficient algorithm for segmenting a face suitable for recognition from a video sequence. We first obtain a coarse face region using skin color, then using dynamic template matching the face is efficiently segmented at varying scale and pose in real time. We have also developed and tested some heuristics which localizes only the face region, even when subjects are wearing skin color dress. The segmented face is then handed over to a recognition algorithm based on principal component analysis and linear discriminant analysis. The on-line face detection, segmentation and recognition algorithm takes an average of 0.06 sec on a 3.2 GHz P4 machine.     

基于DPLS特征提取的LDA方法在玉米近红外光谱定性分析中的应用

提出了一种基于DPLS+LDA的玉米近红外光谱定性分析新方法.该方法在训练时,首先用包含30个玉米品种每个品种20个近红外光谱样本的训练集进行DPLS回归,确定最佳DPLS主成分数为28;然后对训练集光谱进行DPLS特征提取后再进行LDA分析,确定最佳LDA主成分数为26,并提取LDA特征.识别时,测试样本经过DPLS...