Face recognition using kernel entropy component analysis

In this letter, we have reported a new face recognition algorithm based on Renyi entropy component analysis. In the proposed model, kernel-based methodology is integrated with entropy analysis to choose the best principal component vectors that are subsequently used for pattern projection to a lower-dimensional space. Extensive experimentation on Yale and UMIST face database has been conducted to reveal the performance of the entropy based principal component analysis method and comparative analysis is made with the kernel principal component analysis method to signify the importance of selection of principal component vectors based on entropy information rather based only on magnitude of eigenvalues.     

Ensemble learning for independent component analysis

It is well known that the applicability of independent component analysis (ICA) to high-dimensional pattern recognition tasks such as face recognition often suffers from two problems. One is the small sample size problem. The other is the choice of basis functions (or independent components). Both problems make ICA classifier unstable and biased. In this paper, we propose an enhanced ICA algorithm by ensemble learning approach, named as random independent subspace (RIS), to deal with the two problems. Firstly, we use the random resampling technique to generate some low dimensional feature subspaces, and one classifier is constructed in each feature subspace. Then these classifiers are combined into an ensemble classifier using a final decision rule. Extensive experimentations performed on the FERET database suggest that the proposed method can improve the performance of ICA classifier.     

Electrogastrogram extraction using independent component analysis with references

Electrogastrogram (EGG) is a noninvasive measurement of gastric myoelectrical activity cutaneously, which is usually covered by strong artifacts. In this paper, the independent component analysis (ICA) with references was applied to separate the gastric signal from noises. The nonlinear uncorrelatedness between the desired component and references was introduced as a constraint. The results show that the proposed method can extract the desired component corresponding to gastric slow waves directly, avoiding the ordering indeterminacy in ICA. Furthermore, the perturbations in EGG can be suppressed effectively. In summary, it can be a useful method for EGG analysis in research and clinical practice.     

Face recognition using partial least squares components

The paper considers partial least squares (PLS) as a new dimension reduction technique for the feature vector to overcome the small sample size problem in face recognition. Principal component analysis (PCA), a conventional dimension reduction method, selects the components with maximum variability, irrespective of the class information. So PCA does not necessarily extract features that are important for the discrimination of classes. PLS, on the other hand, constructs the components so that the correlation between the class variable and themselves is maximized. Therefore PLS components are more predictive than PCA components in classification. The experimental results on Manchester and ORL databases show that PLS is to be preferred over PCA when classification is the goal and dimension reduction is needed.     

Recognizing facial action units using independent component analysis and support vector machine

Facial expression provides a crucial behavioral measure for studies of human emotion, cognitive processes, and social interaction. In this paper, we focus on recognizing facial action units (AUs), which represent the subtle change of facial expressions. We adopt ICA (independent component analysis) as the feature extraction and representation method and SVM (support vector machine) as the pattern classifier. By comparing with three existing systems, such as Tian, Donato, and Bazzo, our proposed system can achieve the highest recognition rates. Furthermore, the proposed system is fast since it takes only 1.8 ms for classifying a test image.     

Mixture control chart patterns recognition using independent component analysis and support vector machine

Effective recognition of control chart patterns (CCPs) is an important issue since abnormal patterns exhibited in control charts can be associated with certain assignable causes which affect the process. Most of the existing studies assume that the observed process data which needs to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data could be mixture patterns, which consist of two basic CCPs combined together. In this study, a hybrid scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for CCPs recognition. The proposed hybrid ICA-SVM scheme initially applies an ICA to the mixture patterns in order to generate independent components (ICs). The hidden basic patterns of the mixture patterns can be discovered in these ICs. The ICs can then serve as the input variables of the SVM for building a CCP recognition model. Experimental results revealed that the proposed scheme is able to effectively recognize mixture control chart patterns and outperform the single SVM models, which did not use an ICA as a preprocessor.     

Face recognition with one training image per person

At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. projection-combined principal component analysis, (PC)²A, is proposed. (PC)²A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on the enriched version of the image. It requires less computational cost than the standard eigenface technique and experimental results show that on a gray-level frontal view face database where each person has only one training image, (PC)²A achieves 3–5% higher accuracy than the standard eigenface technique through using 10–15% fewer eigenfaces.     

Diagonal principal component analysis for face recognition

In this paper, a novel subspace method called diagonal principal component analysis (DiaPCA) is proposed for face recognition. In contrast to standard PCA, DiaPCA directly seeks the optimal projective vectors from diagonal face images without image-to-vector transformation. While in contrast to 2DPCA, DiaPCA reserves the correlations between variations of rows and those of columns of images. Experiments show that DiaPCA is much more accurate than both PCA and 2DPCA. Furthermore, it is shown that the accuracy can be further improved by combining DiaPCA with 2DPCA.     

Face recognition using the embedded HMM with second-order block-specific observations

The paper is concerned with face recognition using the embedded hidden Markov model (EHMM) with second-order block-specific observations. The proposed method partitions a face image into a 2-D lattice type, composed of many blocks. Each block is represented by the second-order block-specific observation that consists of a combination of first- and second-order feature vectors. The first-order (or second-order) feature vector is obtained by projecting the original (or residual) block image onto the first (or second) basis vector that is obtained block-specifically by applying the PCA to a set of original (or residual) block images. A sequence of feature vectors obtained from the top-to-bottom and the left-to-right scanned blocks are used as an observation sequence to train EHMM. The EHMM models the face image in a hierarchical manner as follows. Several super states are used to model the vertical facial features such as the forehead, eyes, nose, mouth, and chin, and several states in the super state are used to model the localized features in a vertical face feature. Recognition is performed by identifying the person of the model that provides the highest value of observation probability. Experimental results show that the proposed recognition method outperforms many existing methods, such as the second-order eigenface method, the EHMM with DCT observations, and the second-order eigenface method using a confidence factor in terms of average of the normalized modified retrieval rank and false identification rate.     

Data decomposition using independent component analysis with prior constraints

In many data analysis problems, it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make of a system are then taken to be related to these sources through some unknown function. Furthermore, the (unknown) number of underlying latent sources may be less than the number of observations. Recent developments in independent component analysis (ICA) have shown that, in the case where the unknown function linking sources to observations is linear, such data decomposition may be achieved in a mathematically elegant manner. In this paper, we extend the general ICA paradigm to include a very flexible source model, prior constraints and conditioning on sets of intermediate variables so that ICA forms one part of a hierarchical system. We show that such an approach allows for efficient discovery of hidden representation in data and for unsupervised data partitioning.     

Detecting and isolating multiple plant-wide oscillations via spectral independent component analysis

Disturbances that propagate throughout a plant can have an impact on product quality and running costs. There is thus a motivation for the automated detection of plant-wide disturbances and for the isolation of the sources. A new application of independent component analysis (ICA), multi-resolution spectral ICA, is proposed to detect and isolate the sources of multiple oscillations in a chemical process. Its key feature is that it extracts dominant spectrum-like independent components each of which has a narrow-band peak that captures the behaviour of one of the oscillation sources. Additionally, a significance index is presented that links the sources to specific plant measurements in order to facilitate the isolation of the sources of the oscillations. A case study is presented that demonstrates the ability of spectral ICA to detect and isolate multiple dominant oscillations in different frequency ranges in a large data set from an industrial chemical process.     

基于Gabor小波特征的多姿态人脸图像识别

多姿态人脸识别在很多领域具有重要的应用价值。基于多姿态人脸图像及Gabor小波特点选取离散化参数,对人脸图像进行Gabor小波变换;然后采用两步降维法对变换系数进行降维,基于降维后的Gabor特征表示实现人脸识别。实验将互不相交的两个样本集依次作为训练集和测试集,验证了该方法在人脸识别中对于不同姿态和表情的有效性及鲁棒性。     

Face recognition using the second-order mixture-of-eigenfaces method

The well-known eigenface method uses an eigenface set obtained from principal component analysis. However, the single eigenface set is not enough to represent the complicated face images with large variations of poses and/or illuminations. To overcome this weakness, we propose a second-order mixture-of-eigenfaces method that combines the second-order eigenface method (ISO MPG m5750, Noordwijkerhout, March 2000) and the mixture-of-eigenfaces method (a.k.a. Gaussian mixture model (Proceedings IJCNN2001, 2001). In this method, we use a couple of mixtures of multiple eigenface sets: one is a mixture of multiple approximate eigenface sets for face images and another is a mixture of multiple residual eigenface sets for residual face images. Each mixture of multiple eigenface sets has been obtained from expectation maximization learning consecutively. Based on two mixture of multiple eigenface sets, each face image is represented by a couple of feature vectors obtained by projecting the face image onto a selected approximate eigenface set and then by projecting the residual face image onto a selected residual eigenface set. Recognition is performed by the distance in the feature space between the input image and the template image stored in the face database. Simulation results show that the proposed second-order mixture-of-eigenfaces method is best for face images with illumination variations and the mixture-of-eigenfaces method is best for the face images with pose variations in terms of average of the normalized modified retrieval rank and false identification rate.     

电影视频人像检索系统

针对电影视频中序列特征,提出了一种人脸检索的方法及在电影视频中的应用.首先,运用训练好的Cascade Ada-Boost人脸检测器选检测视频序列中的人脸图像,将检测到的人脸图像经标准化处理后投影到增量特征人脸空间中,得到人脸图像的向量表述,应用单类支持向量机进行训练和分类,同时根据分类的结果动态调整增量特征人脸空间方向和分类器的最佳分类超平面,实现对电影视频中特定演员的检索功能,对电影<小花>等的实验表明,这个方法可以得到理想的检索结果.     

Face authentication for multiple subjects using eigenflow

In this paper, we present a novel scheme for face authentication. To deal with variations, such as facial expressions and registration errors, with which traditional intensity-based methods do not perform well, we propose the eigenflow approach. In this approach, the optical flow and the optical flow residue between a test image and an image in the training set are first computed. The optical flow is then fitted to a model that is pre-trained by applying principal component analysis to optical flows resulting from facial expressions and registration errors for the subject. The eigenflow residue, optimally combined with the optical flow residue using linear discriminant analysis, determines the authenticity of the test image. An individual modeling method and a common modeling method are described. We also present a method to optimally choose the threshold for each subject for a multiple-subject authentication system. Experimental results show that the proposed scheme outperforms the traditional methods in the presence of facial expression variations and registration errors.     

Lattice independent component analysis for functional magnetic resonance imaging

We introduce a lattice independent component analysis (LICA) unsupervised scheme to functional magnetic resonance imaging (fMRI) data analysis. LICA is a non-linear alternative to independent component analysis (ICA), such that ICA’s statistical independent sources correspond to LICA’s lattice independent sources. In this paper, LICA uses an incremental lattice source induction algorithm (ILSIA) to induce the lattice independent sources from the input dataset. The ILSIA computes a set of Strongly Lattice Independent vectors using properties of lattice associative memories regarding Lattice Independence and Chebyshev best approximation. The lattice independent sources constitute a set of Affine Independent vectors that define a simplex covering the input data. LICA carries out data linear unmixing based on the lattice independent sources basis. Therefore, LICA is a hybrid combination of a non-linear lattice based component and a linear unmixing component. The principal advantage over ICA is that LICA does not impose any probabilistic model assumptions on the data sources. We compare LICA with ICA in two case studies. Firstly, including simulated fMRI data, LICA discovers the spatial location of meaningful sources with less ambiguity than ICA. Secondly, including real data from an auditory stimulation experiment, LICA improves over some state of the art ICA variants discovering the activation patterns detected by Statistical Parametric Mapping (SPM) on the same data.     

Face recognition using an NNSRM classifier in LDA subspace

NNSRM is an implementation of the structural risk minimization (SRM) principle using the nearest neighbor (NN) rule, and linear discriminant analysis (LDA) is a dimension-reducing method, which is usually used in classifications. This paper combines the two methods for face recognition. We first project the face images into a PCA subspace, then project the results into a much lower-dimensional LDA subspace, and then use an NNSRM classifier to recognize them in the LDA subspace. Experimental results demonstrate that the combined method can achieve a better performance than NN by selecting different distances and a comparable performance with SVM but costing less computational time.

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.     

基于ICA的全局人脸表示

介绍了独立成分分析在全局人脸识别中的应用,重点研究了全局人脸的独立元表示,给出了基于权向量幅值、基于比例因子和基于PCA-ICA算法的3种IC获得途径。基了Umist人脸库的实验结果表明,IC表示识别率明显高于PC表示,其中基于比例因子的IC表示识别效果最好。     

A modular clutter rejection technique for FLIR imagery using region-based principal component analysis

A modular clutter-rejection technique that uses region-based principal component analysis (PCA) is proposed. A major problem in FLIR ATR is the poorly centered targets generated by the preprocessing stage. Our modular clutter-rejection system usesstatic as well as dynamic region of interest (ROI) extraction to overcome the problem of poorly centered targets. In static ROI extraction, the center of the representative ROI coincides with the center of the potential target image. In dynamic ROI extraction, a representative ROI is moved in several directions with respect to the center of the potential target image to extract a number of ROIs. Each module in the proposed system applies region-based PCA to generate the feature vectors, which are subsequently used to make a decision about the identity of the potential target. Region-based PCA uses topological features of the targets to reject false alarms. In this technique, a potential target is divided into several regions and a PCA is performed on each region to extract regional feature vectors. We propose using regional feature vectors of arbitrary shapes and dimensions that are optimized for the topology of a target in a particular region. These regional feature vectors are then used by a two-class classifier based on the learning vector quantization to decide whether a potential target is a false alarm or a real target. We also present experimental results using real-life data to evaluate and compare the performance of the clutter-rejection systems with static and dynamic ROI extraction.