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Principal Component Analysis (PCA) is one of the well-known and widely accepted dimensionality reduction techniques in varied domains. However, PCA does not scale well computationally with increasing dimensionality and it extracts only global features, ignoring local features. The local features may be very useful for classification. More recently, partitioning based PCA approaches (FP-PCA) have been...
In statistical pattern recognition, high dimensionality is a major cause of the practical limitations of many pattern recognition technologies. Moreover, it has been observed that a large number of features may actually degrade the performance of classifiers if the number of training samples is small relative to the number of features. This fact, which is referred to as the “peaking phenomenon”, is...
Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the dimensionality reduction (DR) of face images under the framework of SRC. Although one important merit of SRC is that it is insensitive to...
The high number of features in many machine vision applications has a major impact on the performance of machine learning algorithms. Feature selection (FS) is an avenue to dimensionality reduction. Evolutionary search techniques have been very promising in finding solutions in the exponentially growing search space of FS problems. This paper proposes a genetic programming (GP) approach to FS where...
Principal components analysis (PCA) and linear discriminant analysis (LDA) are the two popular techniques in the context of dimensionality reduction and classification. By extracting discriminant features, LDA is optimal when the distributions of the features for each class are unimodal and separated by the scatter of means. On the other hand, PCA extract descriptive features which helps itself to...
This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used...
We propose a face recognition model consisting of the following stages: facial feature localization (23 essential points, corresponding to eyes, mouth, nose, and face boundary); feature representation by Gabor wavelet based filtering (GWF); dimensionality reduction using principal component analysis (PCA); neural classification using concurrent self-organizing maps (CSOM). For the ORL face database,...
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