Multispectral imaging (MSI): A promising method for the detection of minced beef adulteration with horsemeat
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In this paper, an efficient local appearance feature extraction method based the multi-resolution Steerable Pyramids (SP) transform is proposed in order to further enhance the performance of the well known Fisher Linear Discriminant (FLD) method when applied to face recognition. Each face is described by a subset of band filtered images containing block-based SP coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. The proposed method is compared with some related feature extraction methods such as Principal component analysis (PCA), as well as Linear Discriminant Analysis, and Fisher Linear Discriminant (FLD), Independent Component Analysis and ICA. Experimental results on ORL, YALE and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.
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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 with-class matrix, it can work well when with-class multimodality or outliers exist. Simultaneously, it can remove the limitation of tradition LDA which its embedding space dimension should be less than the number of classes. In this paper, we propose orthogonal local fisher discriminant analysis for facial expression recognition. Experiment on JAFFE database and Cohn-Kanade database show our method can get better performance than PCA, LDA, LFDA.
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Face recognition is characteristically different from regular pattern recognition and, therefore, requires a different discriminant analysis other than linear discriminant analysis(LDA). LDA is a single-exemplar method in the sense that each class during classification is represented by a single exemplar, i.e., the sample mean of the class. We present a multiple-exemplar discriminant analysis (MEDA) where each class is represented using several exemplars or even the whole available sample set. The proposed approach produces improved classification results when tested on a subset of FERET database where LDA is ineffective.
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This paper presents the performance analysis of a methodology for automated recognition of epileptiform patterns using morphological descriptors and Linear Discriminant Analysis. Morphological descriptors, in this paper, are parameters related to the morphology of the signal's waveform and Linear Discriminant Analysis (DA) is a method of multivariate statistical analysis commonly used for classification, size reduction and/or feature extraction. Thus, the main purpose of this paper is to analyze the classification performance of the discriminant functions and examine the applicability of Discriminant Analysis in reducing the number of independent variables (in this case morphological descriptors) necessary to obtain a discriminant function with acceptable classification performance. Simulations showed that the best functions exhibited efficiency greater than or equal to 85%, sensitivity of 85-90% and specificity between 80 and 84%.
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Multiple discriminant analysis
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This paper presents study of face recognition system which is based on Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) [1], [2]. These methods are used for feature extraction and dimension reduction. Nearest Neighbour Classifier (NNC) is used for classification. For matching Mahalanobis Cosine (Mahacos) and Cosine (Cos) distance is used.
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Face recognition is characteristically different from regular pattern recognition and, therefore, requires a different discriminant analysis other than linear discriminant analysis(LDA). LDA is a single-exemplar method in the sense that each class during classification is represented by a single exemplar, i.e., the sample mean of the class. We present a multiple-exemplar discriminant analysis (MEDA) where each class is represented using several exemplars or even the whole available sample set. The proposed approach produces improved classification results when tested on a subset of FERET database where LDA is ineffective.
Optimal discriminant analysis
Multiple discriminant analysis
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Optimal discriminant analysis
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