Discriminant Independent Component Analysis as a subspace representation

Subspace modeling plays an important role in face recognition. Independent Component Analysis （ICA）, a multivariable statistical analysis technique, can be seen as an extension of traditional Principal Com- ponent Analysis （PCA） technique, which addresses high order statistics as well as second order statistics. In this paper, a new scheme of subspace-based representation called Discriminant Independent Component Analysis （DICA） is proposed, which combines the strength＂ of unsupervised learning of ICA and supcrvised learning of Linear Discriminant Analysis （LDA）, and efficiently enhances the generalization ability of ICA-based representation method. Based on DICA subspace analysis, a set of optimal vectors called ＂discriminant independent faces＂ are learned from face samples. The effectiveness of our method is demonstrated by performance comparisons with some popular methods such as ICA, PCA, and PCA＋LDA. On the large scale database of IIS, significant improvements are observed when there are fewer training samples per person available.     

AN EVEN COMPONENT BASED FACE RECOGNITION METHOD

This paper presents a novel face recognition algorithm. To provide additional variations to training data set, even-odd decomposition is adopted, and only the even components （half-even face images） are used for further processing. To tackle with shift-variant problem, Fourier transform is applied to half-even face images. To reduce the dimension of an image, PCA （Principle Component Analysis） features are extracted from the amplitude spectrum of half-even face images. Finally, nearest neighbor classifier is employed for the task of classification. Experimental results on OR.L database show that the proposed method outperforms in terms of accuracy the conventional eigenface method which applies PCA on original images and the eigenface method which uses both the original images and their mirror images as training set.     

A non-parameter bayesian classifier for face recognition

A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE) is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN) classifier in formation. The class conditional density is estimated by KDE and the bandwidth of the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspace analysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA) are respectively used to extract features, and the proposed method is compared with Probabilistic Reasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in face recognition systems. The experiments are performed on two benchmarks an.el the experimental results show that the KDE outperforms PRM, NC and NN classifiers.     

FACE RECOGNITION FROM FRONT－VIEW FACE

This letter presents a face normalization algorithm based on 2-D face model to rec-ognize faces with variant postures from front-view face.A 2-D face mesh model can be extracted from faces with rotation to left or right and the corresponding front-view mesh model can be estimated according to facial symmetry.Then based on the relationship between the two mesh models,the nrmalized front-view face is formed by gray level mapping.Finally,the face recognition will be finished based on Principal Component Analysis(PCA).Experiments show that better face recognition performance is achieved in this way.     

Research on face recognition based on IMED and 2DPCA

This letter proposes an effective method for recognizing face images by combining two-Dimensional Principal Component Analysis （2DPCA） with IMage Euclidean Distance （IMED） method. The proposed method is comprised of four main stages. The first stage uses the wavelet decomposition to extract low frequency subimages from original face images and omits the other three subimages. The second stage concerns the application of IMED to face images. In the third stage, 2DPCA is employed to extract the face features from the processed results in the second stage. Finally, Support Vector Machine （SVM） is applied to classify the extracted face features. Experimental results on the AR face image database show that the proposed method yields better recognition performance in comparison with the 2DPCA method that is not combined with IMED.     

Liveness detection of occluded face based on dual-modality convolutional neural network

Facial recognition has become the most common identity authentication technologies. However, problems such as uneven light and occluded faces have increased the hardness of liveness detection. Nevertheless, there are a few pieces of research on face liveness detection under occlusion conditions. This paper designs a face recognition technique suitable for different degrees of facial occlusion, which employs the facial datasets of near-infrared (NIR) images and visible (VIS) light images to examine the single-modality detection accuracy rate (experimental control group) and the corresponding high-dimensional features through the residual network (ResNet). Based on the idea of data fusion, we propose two feature fusion methods. The two methods extract and fuse the data of one and two convolutional layers from two single-modality detectors respectively. The fusion of high-dimensional features apply a new ResNet to get the dual-modality detection accuracy. And then, a new ResNet is applied to test the accuracy of dual-modality detection. The experimental results show that the dual-modality face liveness detection model improves face live detection accuracy and robustness compared with the single-modality. The fusion of two-layer features from the single-modality detector can also improve face detection accuracy by utilizing the above-mentioned dual-modality detector, and it doesn't increase the algorithm's complexity.     

Real-time Facial Features Tracker Based on Topologic Information and Similarity Analysis

Real-time facial features tracking of video can be widely used in face recognition, video surveillance, face animation and Human-Computer Interaction. We present a fast tracking approach, and our method only requires simple device---a digital camera and a PC, and our approach needs limited user interactions. We first use eigenface and topologic information to detect the position and the size of face from the first frame, and facial features of the first frame are acquired automatically. The successor of the first frame can be tracked by using similarity analysis and motion estimation, the automatic tracker of first frame is also used to resolve the features occlusion problem when the tracked features disappear which is a difficult issue for tracking. Experimental results show that our approach is easily implemented, and the analysis also shows the high robustness of our method.     

A novel fusion method of improved adaptive LTP and two-directional two-dimensional PCA for face feature extraction

In this paper, under different illuminations and random noises, focusing on the local texture feature’s defects of a face image that cannot be completely described because the threshold of local ternary pattern (LTP) cannot be calculated adaptively, a local three-value model of improved adaptive local ternary pattern (IALTP) is proposed. Firstly, the difference function between the center pixel and the neighborhood pixel weight is established to obtain the statistical characteristics of the central pixel and the neighborhood pixel. Secondly, the adaptively gradient descent iterative function is established to calculate the difference coefficient which is defined to be the threshold of the IALTP operator. Finally, the mean and standard deviation of the pixel weight of the local region are used as the coding mode of IALTP. In order to reflect the overall properties of the face and reduce the dimension of features, the two-directional two-dimensional PCA ((2D)2PCA) is adopted. The IALTP is used to extract local texture features of eyes and mouth area. After combining the global features and local features, the fusion features (IALTP+) are obtained. The experimental results on the Extended Yale B and AR standard face databases indicate that under different illuminations and random noises, the algorithm proposed in this paper is more robust than others, and the feature’s dimension is smaller. The shortest running time reaches 0.329 6 s, and the highest recognition rate reaches 97.39%.     

A boosting approach for intrusion detection

Intrusion detection can be essentially regarded as a classification problem, namely, distinguishing normal profiles from intrusive behaviors. This paper introduces boosting classification algorithm into the area of intrusion detection to learn attack signatures. Decision tree algorithm is used as simple base learner of boosting algorithm. Furthermore, this paper employs the Principle Component Analysis （PCA） approach, an effective data reduction approach, to extract the key attribute set from the original high-dimensional network traffic data. KDD CUP 99 data set is used in these experiments to demonstrate that boosting algorithm can greatly improve the classification accuracy of weak learners by combining a number of simple ＂weak learners＂. In our experiments, the error rate of training phase of boosting algorithm is reduced from 30.2% to 8% after 10 iterations. Besides, this paper also compares boosting algorithm with Support Vector Machine （SVM） algorithm and shows that the classification accuracy of boosting algorithm is little better than SVM algorithm＇s. However, the generalization ability of SVM algorithm is better than boosting algorithm.     

基于独立成分分析的高光谱图像数据降维及压缩

该文提出了一种以高光谱图像分析为目标的基于独立成分分析的高光谱图像降维和压缩方法。该方法首先通过独立成分分析提取高光谱数据的光谱特征实现高光谱图像降维,再对降维后的图像采用预测和自适应算术编码的方法进行压缩。对220波段和64波段高光谱数据的实验结果表明,该方法与基于主成分分析的降维相比,压缩比有所提高,特别是更有利于后续的分析处理,但峰值信噪比有所降低。     

基于图像矩阵的广义主分量分析

传统的主分量分析在处理图像识别问题时是基于向量的,且没有充分利用训练样本的类别信息。该文提出了一种直接基于图像矩阵的广义主分量分析方法,该方法能够提取包含在类平均图像中的鉴别信息,与传统的主分量分析相比,具有更强的鉴别力。在ORL标准人脸库上的试验结果表明,所提出的方法不仅识别性能优于传统的主分量分析和Fisher线性鉴别分析,而且极大地提高了特征抽取的速度。     

独立成份分析的信息极大快速算法

本独立成份分析(ICA)是信号处理的一种新的技术,用来从观测的多维混合信号中提取具有统计独立性的成份.本文基于信息极大似然估计,采用牛顿迭代算法,建立了ICA的一种信息极大快速算法.该算法具有二阶收敛性,其有效性为文中展示的图像分离测试效果所证实.     

一种基于加权变形的2DPCA的人脸特征提取方法

该文首先分析了主成分分析法(PCA)和2维主成分分析法(2DPCA)的关系,针对2DPCA丢失具有鉴别能力的协方差信息以及PCA方法不能解决小样本的问题,提出了基于一种加权变形的2DPCA的人脸特征提取方法(WV2DPCA),该方法利用变形的2DPCA方法分别对人脸3个子部分分别提取特征,然后根据最近邻理论和权值进行分类。经过在ORL人脸库和YALE人脸库的实验研究表明：与2DPCA相比,提高了人脸空间的识别率,压缩了人脸空间的系数,减少了识别时间;在识别的准确率方面,更优于传统的Fisherfaces,IC,Kernel Eigenfaces的算法。     

基于递归正交最小二乘的径向基函数网络人脸识别

本文提出了一种基于递归正交最小二乘的径向基函数(RBF)网络人脸识别算法,该算法首先使用主成分分析(PCA)提取输入图像特征,将提取的特征作为RBF网络的输入进行识别,在求取网络权值时采用递归正交最小二乘(ROLS)算法。实验表明,该算法能明显地缩短训练时间同时具有较高的识别率。     

基于高维度特征分析的非局部图像质量评价方法

传统的图像质量评价方法通常提取低维度特征即图像的片面信息用来分析图像质量。高维度特征尽管不易分析但保留了更多信息,更利于全面分析图像质量。针对这种现状,该文提出一种优化数据采样后基于高维度特征分析的图像质量评价方法。首先对图像数据采样分别利用块匹配进行筛选,用主成分分析进行降维,其次利用核独立分量分析从图像数据采样中提取高维度特征,最后基于自然图像统计特性对特征进行综合得出图像质量。实验结果表明所提方法与人的主观评价较为一致。     

基于独立分量分析的高光谱图像目标检测算法

提出一种基于独立分量分析(ICA)的高光谱图像目标检测算法.首先利用无监督正交子空间投影进行端元提取,并将端元矢量构成矩阵作为快速定点独立分量分析的初始化混合矩阵,解决了独立分量在排序上的随机性;同时采用基于噪声调整的主分量分析(NAPCA)对原始图像数据降维,继而采用初始化后的快速独立分量分析从保留的主分量中依次提取出目标.利用AVIRIS高光谱数据进行实验研究,结果表明提出的算法能够有效地提取图像中的目标信息,其性能优于改进的CEM检测算法.     

Fuzzy principal component analysis and its Kernel-based model

Principal Component Analysis(PCA)is one of the most important feature extraction methods,and Kernel Principal Component Analysis(KPCA)is a nonlinear extension of PCA based on kernel methods.In real world,each input data may not be fully assigned to one class and it may partially belong to other classes.Based on the theory of fuzzy sets,this paper presents Fuzzy Principal Component Analysis(FPCA)and its nonlinear extension model,i.e.,Kernel-based Fuzzy Principal Component Analysis(KFPCA).The experimental results indicate that the proposed algorithms have good performances.