基于支持向量机的人脸识别方法研究

对于人脸识别问题，基于K-L变换对人脸图像进行特征参数的提取；并采用支持向量机进行分类．由于支持向量机本身是一个两类问题的判别方法，在处理多类问题时，提出了一种基于支持向量机组的淘汰法，这种方法考虑到了各判别函数的VC置信范围的差异，同时利用判别函数间的冗余来降低识别误差．针对ORL人脸库和自建的人脸库的识别结果表明，基于SVM的识别方法在特征参数个数的选取、识别效果、识别时间等方面都有其独到的优越性．     

基于独立成分分析和核向量机的虹膜识别方法

针对虹膜识别过程中的特征提取及识别问题,提出了用独立成分分析提取虹膜特征,用核向量机进行识别的方法.从采集到的人眼图像中定位虹膜,并对其进行归一化处理和图像增强处理.用独立成分分析提取统计独立的特征,通过选择合适的特征个数可以达到较高的识别准确率.在得到虹膜特征编码后,用核向量机进行分类判决,核向量机是一种适合大规模数据集的快速支持向量机训练算法,并将结果与支持向量机的分类结果进行了对比.实验结果表明了该方法的可行性和有效性.     

基于独立成分分析和核向量机的人脸识别

提出利用独立成分分析提取人脸特征并用核向量机进行识别的方法。独立成分分析能更本质地描述图像特征,通过选择合适的特征个数达到较高的识别准确率。利用核向量机进行分类判决,可以快速地对大样本数据进行准确分类,产生较少的支持向量。实验证明了该方法的可行性和有效性,在ORL人脸数据库上达到了94.38%的准确率。     

基于ICA和GSPSO-SVM的人脸识别方法

针对人脸识别中,利用粒子群算法训练支持向量机进行分类识别时存在易陷入局部最优和收敛速度慢的问题,提出一种基于雁群优化算法的人脸识别方法。将主成分分析与独立成分分析相结合提取人脸特征,利用支持向量机进行分类,在分类识别的过程中,引入雁群优化算法以提高速度和效率。实验结果表明,与标准粒子群算法相比,改进的粒子群算法提高了人脸识别率,具有较快的识别速度。     

支持向量机在人脸识别中的应用

对于人脸识别问题,基于K-L变换对人脸图像进行特征参数的提取,并采用支持向量机进行分类。由于支持向量机本身是一个两类问题的判别方法,在处理多类问题时,提出了一种基于支持向量机组的淘汰法,这种方法考虑到了各判别函数的VC置信范围的差异,同时利用判别函数间的冗余来降低识别误差。在对ORL人脸库和自建的人脸库的测试中,分别得到识别率为97.5%和90.59%的实验结果,这些结果表明,基于SVM的识别方法是有效的。     

结合独立成分分析和核向量机进行人脸识别

在人脸识别过程中,首先利用独立成分分析得到独立的人脸基影像,所提取的特征就是人脸图像在基影像上的投影系数,通过选择合适的特征个数可以达到较高的识别准确率。然后采用支持向量机和核向量机分别对待识别图像在基影像上的投影系数进行分类判决,结果显示二者都能达到较高的识别准确率,但随着特征个数的增加,核向量机的准确率更高,训练时间更短,支持向量更少。实验表明方法可行有效的。     

基于PCA和SVM的步态识别

提出了一种新颖的沿中线投影得到特征的步态识别方法。首先，应用背景差方法分割出运动人体轮廓，对外轮廓沿人体中线投影可以得到前后两个向量，合成1D向量作为步态特征。然后，通过主成分分析对得到的一维向量进行特征提取和压缩，对得到的识别量应用支持向量机进行步态的分类和识别。实验中，该方法取得了很好的识别性能。     

基于小波特征和多类支持向量机的病态语音识别方法

研究一种应用小波特征向量和多类支持向量机进行病态语音识别的方法,该方法基于连续小波变换提取语音特征向量,利用多类支持向量机进行病态语音分类。为了简化二分类支持向量机进行多类分类时所带来的计算复杂性,根据一类支持向量机分类思想提出一种多类分类算法。该算法能够使每一类样本都独立地获得一个决策函数,通过决策函数的最大值来判断样本所属的类。实验表明,在病态语音识别系统中,多类支持向量机与小波特征向量相结合具有良好的识别效果和应用价值。     

基于独立成分分析和信息融合的步态识别

提出基于独立成分分析和多视角信息融合的步态识别方法.应用背景差分和阴影消除检测出人体步态轮廓,对人体轮廓用小波描述子进行特征提取.通过独立成分分析对特征进行压缩,应用支持向量机完成对步态的分类与识别.通过融合不同视角下的步态特征,完成多视角下信息融合的步态识别.方法在NLPR和XAUT步态数据库上进行实验,取得较高的识别率.实验结果表明本文方法具有较高的识别性能.     

基于PCA与ICA特征提取的入侵检测集成分类系统

入侵检测系统不仅要具备良好的入侵检测性能，同时对新的入侵行为要有良好的增量式学习能力．提出了一种入侵检测集成分类系统，将主成分分析（PCA）和独立成分分析（ICA）与增量式支持向量机分类算法相结合构造两个子分类器，采用集成技术对子分类器进行集成．系统利用支持向量集合对已有的入侵知识进行压缩表示，并采用遗传算法自适应地调整集成分类系统的权重．数值实验表明：集成分类系统通过自适应训练权重，综合了两种特征提取子分类器的优点。具有更好的综合性能。     

基于核独立成分分析的人脸识别研究

在人脸识别中提出一种基于非线性子空间的核独立成分分析(KICA)方法。在简单介绍了ICA方法的基础上,对KICA方法的基本原理和算法作了较为详细的描述。为了验证基于KICA和ICA的人脸识别方法的识别效果,进行了对比实验和分析。实验和分析结果表明,在人脸识别中,基于KICA的方法优于基于ICA的方法。     

Face recognition based on 2D Fisherface approach

Two-dimensional (2D) discrimination analysis using methods such as 2D PCA and Image LDA is of interest in face recognition because it extracts discriminative features faster than one-dimensional (1D) discrimination analysis. However, existing 2D methods generally use more discriminative features and take longer to test than 1D methods. 2D PCA in particular cannot make full use of the Fisher discriminant criterion. Image LDA also has drawbacks in that it cannot perform 2D principal component analysis and discards components with poor discriminative capabilities. In addition, existing 2D methods cannot provide an automatic strategy to choose 2D principal components or discriminant vectors. In this paper, we propose 2D Fisherface, a novel discrimination approach that combines the two-stage “PCA+LDA” strategy and 2D discrimination techniques. It can extract face discriminative features by automatically selecting two-dimensional principal components and discriminant vectors. Using the AR database as the test data, it is shown that the proposed approach is faster and more effective than several representative 1D and 2D discrimination methods.     

Improved support vector classification using PCA and ICA feature space modification

An approach that unifies subspace feature selection and optimal classification is presented. Independent component analysis (ICA) and principal component analysis (PCA) provide a maximally variant or statistically independent basis for pattern recognition. A support vector classifier (SVC) provides information about the significance of each feature vector. The feature vectors and the principal and independent component bases are modified to obtain classification results which provide lower classification error and better generalization than can be obtained by the SVC on the raw data and its PCA or ICA subspace representation. The performance of the approach is demonstrated with artificial data sets and an example of face recognition from an image database.     

Face recognition using a fuzzy fisherface classifier

In this study, we are concerned with face recognition using fuzzy fisherface approach and its fuzzy set based augmentation. The well-known fisherface method is relatively insensitive to substantial variations in light direction, face pose, and facial expression. This is accomplished by using both principal component analysis and Fisher's linear discriminant analysis. What makes most of the methods of face recognition (including the fisherface approach) similar is an assumption about the same level of typicality (relevance) of each face to the corresponding class (category). We propose to incorporate a gradual level of assignment to class being regarded as a membership grade with anticipation that such discrimination helps improve classification results. More specifically, when operating on feature vectors resulting from the PCA transformation we complete a Fuzzy K-nearest neighbor class assignment that produces the corresponding degrees of class membership. The comprehensive experiments completed on ORL, Yale, and CNU (Chungbuk National University) face databases show improved classification rates and reduced sensitivity to variations between face images caused by changes in illumination and viewing directions. The performance is compared vis-à-vis other commonly used methods, such as eigenface and fisherface.     

A novel hybrid approach based on sub-pattern technique and whitened PCA for face recognition

Recently, in a task of face recognition, some researchers presented that independent component analysis (ICA) Architecture I involves a vertically centered principal component analysis (PCA) process (PCA I) and ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). They also concluded that the performance of ICA strongly depends on its involved PCA process. This means that the computationally expensive ICA projection is unnecessary for further process and involved PCA process of ICA, whether PCA I or II, can be used directly for face recognition. But these approaches only consider the global information of face images. Some local information may be ignored. Therefore, in this paper, the sub-pattern technique was combined with PCA I and PCA II, respectively, for face recognition. In other words, two new different sub-pattern based whitened PCA approaches (which are called Sp-PCA I and Sp-PCA II, respectively) were performed and compared with PCA I, PCA II, PCA, and sub-pattern based PCA (SpPCA). Then, we find that sub-pattern technique is useful to PCA I but not to PCA II and PCA. Simultaneously, we also discussed what causes this result in this paper. At last, by simultaneously considering global and local information of face images, we developed a novel hybrid approach which combines PCA II and Sp-PCA I for face recognition. The experimental results reveal that the proposed novel hybrid approach has better recognition performance than that obtained using other traditional methods.     

一种对角LDA算法及其在人脸识别上的应用

2维特征抽取方法(如2DPCA、2DLDA),因为其抽取特征的速度和识别率要比1维的方法好,所以在人脸识别中得到了广泛的应用。最近基于2DPCA又提出了对角主成份分析(diagonal principal component analysis,DiaPCA),该方法由于保持了图像的行变化和图像的列变化之间的相关性,从而克服了2DPCA仅能反映图像行之间的变化,而忽略了图像列之间变化的缺点。但是,由于DiaPCA并没在特征抽取中融入鉴别信息,同时2DLDA也具有与2DPCA同样的缺点,从而分别影响了DiaPCA与2DLDA两种方法的识别性能。针对这一问题,提出了一种对角线性鉴别分析(diagonal linear dicriminant analysis,DiaLDA)的新算法,该新算法是基于对角人脸图像来求解最优鉴别向量。该新算法在ORL和FERET人脸库进行了实验,并与PCA、Fisherface、DiaPCA、2DLDA等方法进行了比较。实验结果表明,该方法比其他方法的识别性能要好。     

改进的PCA-LDA人脸识别算法的研究

主成分分析算法(PCA)和线性鉴别分析算法(LDA)被广泛用于人脸识别技术中,但是PCA由于其计算复杂度高,致使人脸识别的实时性达不到要求。线性鉴别分析算法存在“小样本”和“边缘类”问题,降低了人脸识别的准确性。针对上述问题,提出使用二维主成分分析法(2DPCA)与改进的线性鉴别分析法相融合的方法。二维主成分分析法提取的特征比一维主成分分析法更丰富,并且降低了计算复杂度。改进的线性鉴别分析算法重新定义了样本类间离散度矩阵和Fisher准则,克服了传统线性鉴别分析算法存在的问题,保留了最有辨别力的信息,提高了算法的识别率。实验结果表明,该算法比主成分分析算法和线性鉴别分析算法具有更高的识别率,可以较好地用于人脸识别任务。     

Color face recognition based on quaternion matrix representation

There are several methods to recognize and reconstruct a human face image. The principal component analysis (PCA) is a successful approach because of its effective extraction of the global feature and excellent reconstruction of face image. However, the crucial shortcomings of PCA are its low recognition rate and overfitting of feature extraction which leads to the dependence of training data on training samples. In this paper, a modified two-dimension principal component analysis (2DPCA) and bidirectional principal component analysis (BDPCA) methods based on quaternion matrix are proposed to recognize and reconstruct a color face image. In these methods, the spatial distribution information of color images is used to represent a color face, and the 2DPCA or BDPCA feature of color face image is extracted by reducing the dimensionality in both column and row directions. A method obtaining orthogonal eigenvector set of quaternion matrix is proposed. Numerous experiments show that the present approach based on quaternion matrix can effectively smooth the overfitting issue and substantially enhance the recognition rate.     

一种基于2D-DWT和2D-PCA的人脸识别方法

提出了一种联合图像二维离散小波变换（2D-DWT）和二维主成分分析（2D-PCA）的人脸识别方法。首先通过2D-DWT将当前图像分解成四个子图像，其中一子图像对应原图像的主体部分（低通部分），其余三个子图像则对应图像的细节部分（高通部分）。在此基础上，采用2D-PCA方法分别对每一子图像进行特征提取。此外，文中还提出了一种简单有效的方法对各子图像中所提取的特征进行融合，根据所得到的特征进行人脸识别。同其他基于小波分解的人脸识别方法相比，所提出的方法能更充分地利用人脸图像的有用判别信息，并得到更好的识别结果。