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In this paper, a frequency domain feature extraction algorithm for palm-print recognition is proposed, which efficiently exploits the local spatial variations in a palm-print image. The entire image is segmented into several narrow-width spatial bands and a palm-print recognition scheme is developed based on extracting dominant spectral features from each of these bands using two-dimensional discrete...
This paper presents an off-line signature verification system composed of a combination of several different classifiers. Identity authentication is a very important characteristics specially in systems that requires a high degree of security such as in bank transactions. In our experiments, one-class classifier was used to create a signature verification system, consequently only genuine signatures...
Typically, two aspects are used to evaluate the quality of a classification model, i.e., the classifying accuracy and the interpretability. The recently developed sparse representation-based face recognition techniques, though achieving high accuracies, rarely concern the interpretability of the classification model. In particular, the obtained sparseness, in terms of the sparse representative coefficient...
Moving sequential pattern mining is important in the trajectory analysis of moving objects and mobility prediction in mobile environment. The traditional sequential pattern mining method-PrefixSpan, is not applicable to the spatial constrained applications and it will produce a large number of duplicated projected databases in mining data sets. In order to overcome these drawbacks, a new algorithm...
Face recognition has received an increased attention from several years in the field of image analysis, pattern recognition, and computer vision. In this paper we propose a method to the problem of face recognition. The proposed method consists of two stages. In the first stage regularized linear discriminant analysis is used to extract the most significant and discriminant features and then in the...
Ground-based cloud recognition plays an essential role for automatic cloud observation. In particular, the recognition of clouds is remarkably challenging because that the shape, size, and composition of cloud is extremely variable under different atmospheric conditions. A new method is proposed to extract texturral feature using Bidimensional Empirical Mode Decomposition(BEMD) and Tamura textural...
The disaster information mapping templates developed according to the distribution of the earthquake and the secondary disasters can improve the thematic mapping efficiency. It can also provide accurate disaster information of disaster and affected body quickly. First, Disaster special subject sign database should be designed, including disaster category symbols, loss assessment category symbols,...
A face hallucination scheme based on independent residual features (IRFs) is proposed in this paper. In the proposed scheme, a high-resolution (HR) face image is assumed as composition of two parts: an approximate and a residual image, where the approximate image is obtained by interpolating the corresponding low-resolution (LR) face image. According to this assumption, a residual image training database...
In this paper, an improved Gradientface method is proposed for face recognition under varying illumination. It uses the gradient angle as the input feature. It generates the gradient vectors in difference form, and then computes the gradient angle. The gradient angle which is computed by differential equation preserves the detailed image information and it is proved to be most insensitive to the illumination...
This paper proposes a novel weighted distance metric based on 2D matrices rather than 1D vectors and the eigenvalues for face images classification and recognition. This distance is measured between two feature matrices obtained by two-dimensional principal component analysis (2DPCA) and two-dimensional linear discriminant analysis (2DLDA). The weights are the inverse of the eigenvalues of the total...
In order to overcome the shortcomings of Principal Component Analysis (PCA) and bionic methods in feature extracting and dimension reduction, a method for extracting Gabor features of face images based on Gabor wavelet is presented. First, Gabor features are extracted from face images. After reduced by 2DPCA algorithm, the features are reduced further by rough set. Then the nearest classifier is trained...
Traditional graph construction methods usually decompose the graph construction process into two steps and have certain parameters which required manual setting. In addition, since changes in pose, illumination and expression would cause large changes in the appearance, this often leads to the fact that neighboring samples don't belong to the same class. However, these faults are overcome by the LI...
Changchun tourism information releasing system is categorized WebGIS. The system is linked by the SQL Server 2000 database management system which is based on the middleware ArcSDE, the client's browser which the system selects is the HTML browser, and the server end includs the Web server, the GIS server and the space database server. The character of this system is: the user could get the following...
Classical Karhunen-Loeve(K-L) transformation is based on Euclidean distance, and Euclidean distance is sensitive to outlier. In many cases, cosine angle distance has better performance than Euclidean distance. In this paper, a K-L transformation based on cosine angle distance (K-L-C) algorithm is proposed. K-L-C transformation uses cosine angle distance to measure the error of data reconstruction...
In recent years, feature extraction method make an achievement in pattern recognition. It extracts not only useful feature for classification, but also reduces the dimension of pattern sample. Linear discriminant analysis is an important method for image recognition, it achieve significant development both in theory and applications. Local fisher discriminant analysis redefines the between-class and...
Face recognition has a great demands in human authentication and it becomes one of the most intensive field of biometrics research areas. In this paper, we present a bio-inspired face recognition system based on linear discriminant analysis and external clue i.e. geometrical features. The use of external clue helps to identify the face among very close match and secondly it also helps in the creation...
A novel approach to recognize facial expressions from static images is proposed in this paper. The local binary pattern (LBP) operator is adopted as an effective feature extraction tool for facial image data. An unsupervised competitive neural network, called a centroid neural network with x2 distance measure, CNN-x2, is then utilized as the classification tool for the histogram data obtained by the...
This paper deals with stochastic texture modeling for classification issue. A generic stochastic model based on three-parameter Generalized Gamma (GG) distribution function is proposed. The GG modeling offers more flexibility parameterization than other kinds of heavy-tailed density devoted to wavelet empirical histograms characterization. Moreover, Kullback-leibler divergence is chosen as similarity...
We present a face recognition technique based on the sparsity principle. Parsimony is used both to compute the face feature vectors and to process the classification of these vectors. Applied to visible and infrared modalities on the Notre-Dame database, we show that this approach has equal or better performances than those of the state-of-art on this database. This classification allows to use a...
In this paper we present a novel image classification methodology based on texture signature. The approach consists of four distinct steps: 1) feature extraction from texture images without using any prior knowledge (e.g. viewpoint, illumination condition); 2) textures are modelled as texture signatures; 3) model selection and reduction is used to remove noise and outliers; 4) texture image classification...
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