自适应通用学习框架改进FLDA的人脸识别

针对传统的Fisher线性判别分析(FLDA)算法在处理单训练样本人脸识别时由于类内散布矩阵为零而不能进行特征提取的问题,提出了一种基于自适应通用学习框架改进FLDA的人脸识别算法。首先选取一个合适的通用训练样本集,计算其类内散布矩阵和样本平均向量;然后,利用双线性表示算法预测单训练样本的类内、类间散布矩阵,巧妙地解决了单训练样本类内散布矩阵为零的问题;最后,利用Fisher线性判别分析进行特征提取,同时借助于最近邻分类器完成人脸的识别。在Yale及FERET两大通用人脸数据库上的实验验证了所提算法的有效性及可靠性,实验结果表明,相比其他几种较为先进的单样本人脸识别算法,所提算法取得了更好的识别效果。     

改进的双向2DPCA的人脸识别方法

为了提高人脸识别的准确率,且考虑到训练样本的平均值不一定是训练样本分布中心,提出了改进的双向2DPCA人脸识别方法。首先,应用样本中间值代替样本的平均值来重建图像的总体散布矩阵,求解图像总体散布矩阵得到行列两个方向的最优投影向量,然后把人脸图像向这两个方向变换得到人脸识别特征矩阵,最后应用支持向量机进行分类识别。在ORL人脸库和Yale人脸库上对该算法进行实验研究,表明此方法在识别性能上优于普通的二维主成分分析和普通的双向二维主成分分析算法。     

基于小波变换的人脸识别技术研究

文章提出了基于小波分解与PCA变换相结合的人脸特征提取方法,首先对人脸图像进行二维小波变换,对变换后的低频分量做PCA变换以提取人脸特征,最后,使用SVM分类器进行人脸识别。为了验证方法的有效性,文章以ORL人脸库作为测试数据库,前5幅人脸作为训练样本,后5幅人脸作为测试样本,实验结果表明该方法能有效的识别处理过的人脸图像。     

基于图像隶属度的主分量分析人脸识别算法

提出了基于图像隶属度的主分量人脸识别算法.该算法首先用小波变换对人脸图像进行小波分解,形成低频小波子图,然后用主分量分析法构造特征脸子空间.计算训练样本和待测样本在人脸特征空间中的投影向量间的距离.引入图像隶属函数,作为识别分类器进行人脸识别.针对ORL人脸库的实验结果表明该方法具有良好的识别分离能力.     

基于分块小波变换和2DPCA的人脸特征提取与识别算法

从最优化的角度出发,提出了一种基于分块小波变换和二维主成分分析法(2DPCA)的人脸特征提取与识别算法。该方法首先对人脸图像进行分块小波变换,并对各分块的高、低频分量进行组合处理,然后对小波系数特征应用2DPCA方法进行变换并将分块特征进行融合得到人脸鉴别特征,最后在ORL人脸库上应用支持向量机(SVM)对该特征进行分类识别。试验结果表明,该算法能有效地提高人脸识别性能,具有较短的识别时间和较高的识别准确率,优于传统的人脸识别方法。     

基于判别性降维的字典学习在人脸识别中的应用

针对目前大多数人脸识别方法只能单独实施降维或者字典学习而不能完全利用训练样本判别信息的问题,提出了基于判别性降维的字典学习方法,通过联合降维与字典学习使得投影矩阵和字典更好地相互拟合,从而可以获得更高效的人脸分类系统。所提方法的有效性在AR及MPIE两大通用人脸数据库上得到了验证,实验结果表明,相比于几种先进的线性表示方法,所提算法取得了更高的识别率,特别当训练样本数很少的时候,识别效果更佳。     

基于K-L变换和平均近邻法的人脸识别

首先介绍了K-L变换和近邻法判别的原理;然后以MATLAB R2009b为实验平台,分别利用类间散布矩阵和总体散布矩阵作为K-L变换的的产生矩阵,对ORL人脸库的400幅图片进行K-L变换,一部分作为训练样本,一部分作为待识别样本,训练样本以产生特征脸空间;接着计算出待识别图片在特征脸空间的坐标,采用平均近邻法进行人脸识别.最终的实验结果给出了基于两种产生矩阵的算法时间和正确识别率,实验证明采用K-L变换对人脸提取特征很有效,本文基于K-L变换和平均近邻判别法的人脸识别的方法正确率最高可达到95％.     

一种半监督稀疏保持近邻判别嵌入算法

保持近邻嵌入（NPE）算法对局部线性嵌入（LLE）算法进行了改进,克服了新来样本问题,但在处理分类问题上表现不足。本文提出了一种半监督稀疏保持近邻判别嵌入算法,该方法首先采用小波变换对数据进行预处理,然后执行等距离映射(Isomap)算法选择合适的低维嵌入维数,最后结合稀疏表示理论、NPE和线性判别分析（LDA）的思想,重构邻域图,并在建立目标函数时使得已标签信息中同类样本点之间相互靠近,异类样本点之间相互远离,未标签信息邻域信息得以保持,这样,既得到了高维映射函数,又提高了分类正确率。通过在人脸数据库上实验,并和其他半监督算法作比较,本文提出的算法在识别率上表现较好。     

基于Gabor的特征提取算法在人脸识别中的应用

针对人脸识别中的特征提取问题,提出一种新的基于Gabor的特征提取算法,利用Gabor小波变换良好的提取区分能力和LDA所具有的判别性优势来进行特征提取。首先利用Gabor小波变换来提取人脸特征。然后对得到的高维特征采用PCA进行初次降维,再利用LDA实现再次降维,得到最终的特征向量。在ORL和YALE人脸库上的实验验证了该算法的有效性。     

基于掌纹、人脸关联特征的身份识别算法

首先对掌纹、人脸图像进行融合;接着利用小波变换增强融合后图像;然后利用一种新的子空间分析方法--对角离散余弦变换和二维主元判别分析(Diagonal,Discrete Cosine Traasform and Two-Dimensional Principle Component Analysis,Dia-DCT+2DPCA)相结合的算法进行特征提取;最后运用最小距离分类器进行识别.实验结果表明,本文算法有效地提高了身份识别的正确识别率.     

基于小波域两向二维主分量分析的SAR目标识别

针对合成孔径雷达图像目标识别在图像域进行特征提取时空间维数较高、计算复杂度较大、识别效率较低等问题,提出基于小波域两向二维主分量分析和概率神经网络的SAR图像目标特征提取与识别方法。该方法首先引入二维离散小波变换将预处理后的SAR图像变换到小波域,得到可充分表征目标特征信息的低频成分。然后提取低频子图像的两向二维主分量分析低维特征作为训练样本和测试样本的目标特征,最后由概率神经网络分类器完成目标识别。MSTAR数据实验结果表明,在特征矩阵维数低至6×3(原始图像128×128)的情况下平均识别率高达99.32%,且最高可达99.83%,该方法不但能够有效压缩目标特征维数和提高识别率,还对目标的方位信息具有很强的鲁棒性,可有效应用于SAR图像目标特征提取和识别。     

Local appearance based face recognition method using block based steerable pyramid transform

In this paper, an efficient local appearance feature extraction method based on Steerable Pyramid (S-P) wavelet transform is proposed for face recognition. Local information is extracted by computing the statistics of each sub-block obtained by dividing S-P sub-bands. The obtained local features of each sub-band are combined at the feature and decision level to enhance face recognition performance. The purpose of this paper is to explore the usefulness of S-P as feature extraction method for face recognition. The proposed approach is compared with some related feature extraction methods such as principal component analysis (PCA), as well as linear discriminant analysis LDA and boosted LDA. Different multi-resolution transforms, wavelet (DWT), gabor, curvelet and contourlet, are also compared against the block-based S-P method. Experimental results on ORL, Yale, Essex and FERET face databases convince us that the proposed method provides a better representation of the class information, and obtains much higher recognition accuracies in real-world situations including changes in pose, expression and illumination.     

基于无穷范数的二值线性判别分析

线性判别分析（LDA）是监督式的特征提取方法,在人脸识别等领域得到了广泛应用。为了提高特征提取速度,提出了基于无穷范数的线性判别分析方法。传统LDA方法将目标函数表示为类内散布矩阵和类间散布矩阵之差的或者商的L2范数,且通常需要涉及到矩阵求逆和特征值分解问题。与传统方法不同,这里所提方法将目标函数表示为类内散布矩阵和类间散布矩阵之差的无穷范数,而且最优解是以迭代形式得到,避免了耗时的特征值分解。无穷范数使得到的基向量实现了二值化,即元素仅在-1和1两个数字内取值,避免了特征提取时的浮点型点积运算,从而降低了测试时间,提高了效率。在ORL人脸数据库和Yale数据库上的实验表明所提算法是有效的。     

Spare L1-norm-based maximum margin criterion

Maximum margin criterion (MMC) is a popular method for dimensionality reduction or feature extraction. MMC can alleviate the small size sample (SSS) problem encountered by linear discriminant analysis (LDA) and extract more discriminant vectors than LDA. However, the objective function of MMC is derived from L2-norm, which makes MMC be sensitive to noise and outliers. Besides, the basis vectors of MMC are dense, which makes it hard to explain the obtained features. To address the drawbacks of MMC, in this paper, we propose a novel sparse L1-norm-based maximum margin criterion (SMMC-L1). L1-norm rather than L2-norm is used in the objective function of SMMC-L1. Besides, L1-norm is also used as a lasso penalty to regularize the basis vectors. An iterative algorithm for solving SMMC-L1 is proposed. Experiment results on some databases show the effectiveness of the proposed SMMC-L1.     

A Supervised Feature-Projection-Based Real-Time EMG Pattern Recognition for Multifunction Myoelectric Hand Control

Electromyographic (EMG) pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study was to develop an efficient feature- projection method for EMG pattern recognition. To this end, a linear supervised feature projection is proposed that utilizes a linear discriminant analysis (LDA). First, a wavelet packet transform (WPT) is performed to extract a feature vector from four-channel EMG signals. To dimensionally reduce and cluster the WPT features, an LDA, then, incorporates class information into the learning procedure, and identifies a linear matrix to maximize the class separability for the projected features. Finally, a multilayer perceptron classifies the LDA-reduced features into nine hand motions. To evaluate the performance of the LDA for WPT features, the LDA is compared with three other feature-projection methods. From a visualization and quantitative comparison, it is shown that the LDA produces a better performance for the class separability, plus the LDA-projected features improve the classification accuracy with a short processing time. A real-time pattern-recognition system is then implemented for a multifunction myoelectric hand. Experiments show that the proposed method achieves a 97.4% recognition accuracy, and all processes, including the generation of control commands for the myoelectric hand, are completed within 97 ms. Consequently, these results confirm that the proposed method is applicable to real-time EMG pattern recognition for multifunction myoelectric hand control.     

基于两空间核鉴别分析的人脸识别

指出了几种线性鉴别分析方法在处理小样本人脸识别问题时存在的不足,结合核方法的思想,提出了一种基于两空间核鉴别分析的人脸识别方法.首先使用KPCA方法在核变换后的特征空间中对样本进行处理;进面将变换后的类内散布矩阵分成非零空间和零空间进行鉴别向量确定和鉴别特征提取,最后将得到的两种鉴别特征融合,从而使用最近邻法进行分类....     

Relevance‐Weighted (2D)2LDA Image Projection Technique for Face Recognition

In this paper, a novel image projection technique for face recognition application is proposed which is based on linear discriminant analysis (LDA) combined with the relevance‐weighted (RW) method. The projection is performed through 2‐directional and 2‐dimensional LDA, or (2D)²LDA, which simultaneously works in row and column directions to solve the small sample size problem. Moreover, a weighted discriminant hyperplane is used in the between‐class scatter matrix, and an RW method is used in the within‐class scatter matrix to weigh the information to resolve confusable data in these classes. This technique is called the relevance‐weighted (2D)²LDA, or RW(2D)²LDA, which is used for a more accurate discriminant decision than that produced by the conventional LDA or 2DLDA. The proposed technique has been successfully tested on four face databases. Experimental results indicate that the proposed RW(2D)²LDA algorithm is more computationally efficient than the conventional algorithms because it has fewer features and faster times. It can also improve performance and has a maximum recognition rate of over 97%.     

Gabor feature-based face recognition using supervised locality preserving projection

This paper introduces a novel Gabor-based supervised locality preserving projection (GSLPP) method for face recognition. Locality preserving projection (LPP) is a recently proposed method for unsupervised linear dimensionality reduction. LPP seeks to preserve the local structure which is usually more significant than the global structure preserved by principal component analysis (PCA) and linear discriminant analysis (LDA). In this paper, we investigate its extension, called supervised locality preserving projection (SLPP), using class labels of data points to enhance its discriminant power in their mapping into a low-dimensional space. The GSLPP method, which is robust to variations of illumination and facial expression, applies the SLPP to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. We performed comparative experiments of various face recognition schemes, including the proposed GSLPP method, PCA method, LDA method, LPP method, the combination of Gabor and PCA method (GPCA) and the combination of Gabor and LDA method (GLDA). Experimental results on AR database and CMU PIE database show superior of the novel GSLPP method.     

A Novel Method for the Diagnosis of the Incipient Faults in Analog Circuits Based on LDA and HMM

Diagnosis of incipient faults for electronic systems, especially for analog circuits, is very important, yet very difficult. The methods reported in the literature are only effective on hard faults, i.e., short-circuit or open-circuit of the components. For a soft fault, the fault can only be diagnosed under the occurrence of large variation of component parameters. In this paper, a novel method based on linear discriminant analysis (LDA) and hidden Markov model (HMM) is proposed for the diagnosis of incipient faults in analog circuits. Numerical simulations show that the proposed method can significantly improve the recognition performance. First, to include more fault information, three kinds of original feature vectors, i.e., voltage, autoregression-moving average (ARMA), and wavelet, are extracted from the analog circuits. Subsequently, LDA is used to reduce the dimensions of the original feature vectors and remove their redundancy, and thus, the processed feature vectors are obtained. The LDA is further used to project three kinds of the processed feature vectors together, to obtain the hybrid feature vectors. Finally, the hybrid feature vectors are used to form the observation sequences, which are sent to HMM to accomplish the diagnosis of the incipient faults. The performance of the proposed method is tested, and it indicates that the method has better recognition capability than the popularly used backpropagation (BP) network.     

基于二维子分类鉴别分析的SAR图像识别方法研究

 本文在分析传统二维鉴别分析方法局限性的基础上,提出了一种基于二维子分类鉴别分析的合成孔径雷达图像识别方法。该方法首先对SAR图像进行图像预处理,然后利用图像欧氏距离对每类目标进行子类划分,并由图像的行信息和列信息提出了两种二维子分类鉴别分析方法,最后利用最近邻分类器对提取的特征投影矩阵进行分类识别。本文利用美国实测的MSTAR数据对算法进行了仿真验证,实验结果表明了本文方法的正确性和有效性。