基于亮度检测检测和SVM的人脸表情识别算法

针对包含表情信息的静态图像,提出基于皮肤检测和SVM的人脸表情识别算法。首先根据先验知识,并使用皮肤检测和积分投影相结合定位眉毛眼睛区域和嘴巴区域,自动分割出表情子区域。接着,对分割出的表情子区域进行Gabor小波特征提取,在利用Fisher线性判别对特征进行降维,去除冗余和相关。最后利用支持向量机对人脸表情进行分类。用该算法在日本表情数据库上进行测试,获得了较高的识别准确率。证明了该算法的有效性。     

Factors affecting clients' project dispute resolution satisfaction in Hong Kong

The use of project dispute resolution satisfaction (DRS) as a project success measurement responds to the increasing concern over the spread of the dispute epidemic within the construction industry in Hong Kong. The DRS of a project is considered as favourable where disputes are resolved within the site level. With the use of 48 project data sets and the technique of multivariate discriminant analysis, a discriminant model function was derived to classify projects into favourable DRS and adverse DRS. The multivariate discriminant analysis model function identified eight discriminating variables, among which the degree of design changes offered the highest separation power. The reliability of the discrminating variables was reinforced by a separate relative importance index study. Through the use of principal component factor analysis, these variables were grouped into three factors generically described as substantive influence, facilitation and indirect influence.     

Iterated discriminants

It is shown that the discriminant of the discriminant of a multivariate polynomial has the same irreducible factors as the product of seven polynomials each of which is defined as the GCD of the generators of an elimination ideal. Under relatively mild conditions of genericity, three of these polynomials are irreducible and generate the corresponding elimination ideals, while the other four are equal to one. Moreover the irreducible factors of two of these polynomials have multiplicity at least two in the iterated discriminant and the irreducible factors of two others of the seven polynomials have multiplicity at least three.     

Self-supervised learning based on discriminative nonlinear features for image classification

For learning-based tasks such as image classification, the feature dimension is usually very high. The learning is afflicted by the curse of dimensionality as the search space grows exponentially with the dimension. Discriminant-EM (DEM) proposed a framework by applying self-supervised learning in a discriminating subspace. This paper extends the linear DEM to a nonlinear kernel algorithm, Kernel DEM (KDEM), and evaluates KDEM extensively on benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated for several tasks of image classification. Extensive results show the effectiveness of our approach.     

Using discriminant analysis for multi-class classification: an experimental investigation

Many supervised machine learning tasks can be cast as multi-class classification problems. Support vector machines (SVMs) excel at binary classification problems, but the elegant theory behind large-margin hyperplane cannot be easily extended to their multi-class counterparts. On the other hand, it was shown that the decision hyperplanes for binary classification obtained by SVMs are equivalent to the solutions obtained by Fisher's linear discriminant on the set of support vectors. Discriminant analysis approaches are well known to learn discriminative feature transformations in the statistical pattern recognition literature and can be easily extend to multi-class cases. The use of discriminant analysis, however, has not been fully experimented in the data mining literature. In this paper, we explore the use of discriminant analysis for multi-class classification problems. We evaluate the performance of discriminant analysis on a large collection of benchmark datasets and investigate its usage in text categorization. Our experiments suggest that discriminant analysis provides a fast, efficient yet accurate alternative for general multi-class classification problems. Tao Li is currently an assistant professor in the School of Computer Science at Florida International University. He received his Ph.D. degree in Computer Science from University of Rochester in 2004. His primary research interests are: data mining, machine learning, bioinformatics, and music information retrieval. Shenghuo Zhu is currently a researcher in NEC Laboratories America, Inc. He received his B.E. from Zhejiang University in 1994, B.E. from Tsinghua University in 1997, and Ph.D degree in Computer Science from University of Rochester in 2003. His primary research interests include information retrieval, machine learning, and data mining. Mitsunori Ogihara received a Ph.D. in Information Sciences at Tokyo Institute of Technology in 1993. He is currently Professor and Chair of the Department of Computer Science at the University of Rochester. His primary research interests are data mining, computational complexity, and molecular computation.     

On detecting nonlinear patterns in discriminant problems

We propose a two-stage model for detecting nonlinear patterns in discriminant problems and for solving the problem. The model deploys a Linear Programming Based Discriminator (LPBD) in stage one for treating linear patterns and a Probabilistic Neural Network (PNN) in stage two for handling nonlinear patterns. The LPBD in stage one divides the decision space into a clear zone where observations are (almost) linearly separable and a gray zone where nonlinear patterns are more likely to occur. The PNN in stage two analyzes the gray zone and determines whether a significant nonlinear patterns exist in the observations. Our goal is to avoid using a nonlinear model unless the PNN strongly suggests so to maintain good interpretability and avoid overfitting. Our computational study demonstrates the effectiveness of the two-stage model in both classification accuracy and computational efficiency.     

A characterization of nearest-neighbor rule decision surfaces and a new approach to generate them

The paper considers generating nearest-neighbor rule decision surfaces as an application of a maxmin problem. The maxmin problem is to locate a point in a given convex polyhedron which maximizes the minimum distance from a given set of points in the polyhedron. A characterization of the decision surfaces in n-dimensions is given, and the difficulty involved in generating the decision surfaces in higher dimensional spaces is brought out through this characterization. However, a novel method is presented to generate the surfaces in three dimensions using the algorithm for the maxmin problem.     

A general method for training the committee machine

A method for training the committee machine with an arbitrary logic is described. First, an expression of the discriminant function realized by the committee machine is introduced. By making use of the expression, an error-correction procedure for training the committee machine is proposed. The procedure of the perceptron is clearly explained as the special case of the proposed procedure. Experimental results show that the procedure is effective.     

Evaluation of the effectiveness of features selected by discriminant analysis methods

New theoretical and practical results concerning the use of discriminant analysis for feature selection are presented in the paper. Numerical values for the eigenvalues of the matrix SW_W⁻¹ S_B (within-class and between-class scatter matrices) are investigated. An analytic expression for their minimum value representing the minimum effectiveness is derived. Differences between real values and these minimum values are important for the evaluation of the effectiveness of features and thus for feature selection.     

Handwriting style classification

This paper describes an independent handwriting style classifier that has been designed to select the best recognizer for a given style of writing. For this purpose a definition of handwriting legibility has been defined and a method implemented that can predict this legibility. The technique consists of two phases. In the feature-extraction phase, a set of 36 features is extracted from the image contour. In the classification phase, two nonparametric classification techniques are applied to the extracted features in order to compare their effectiveness in classifying words into legible, illegible, and middle classes. In the first method, a multiple discriminant analysis (MDA) is used to transform the space of extracted features (36 dimensions) into an optimal discriminant space for a nearest mean based classifier. In the second method, a probabilistic neural network (PNN) based on the Bayes strategy and nonparametric estimation of probability density function is used. The experimental results show that the PNN method gives superior classification results when compared with the MDA method. For the legible, illegible, and middle handwriting the method provides 86.5% (legible/illegible), 65.5% (legible/middle), and 90.5% (middle/illegible) correct classification for two classes. For the three-class legibility classification the rate of correct classification is 67.33% using a PNN classifier.Received: 6 September 2002, Accepted: 19 September 2002, Published online: 6 June 2003