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In this paper, a new multi-dimensional facial recognition system is proposed. A new technique for data reduction for multidimensional biometric facial analysis to improve face recognition performance in real environments is implemented. For this the tensorial methods are adopted, the sample of the face must be reshaped by natural tensor representations into vectors of very large dimensions. This remodeling...
Infrared face recognition, being light-independent, and not vulnerable to facial skin, expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. Local binary pattern (LBP), as a classic local feature descriptor, is appreciated for infrared face feature representation. To extract discriminative subset in LBP features, infrared face recognition based on optimized...
This paper proposes to use Genetic algorithm for optimizing the best Eigen vectors to improve the recognition accuracy of Modular image Principal Component Analysis (MIPCA) for face recognition. Modular Image PCA has been proved to be efficient in extracting features for recognizing face invariant to large expression. It is important to note that all the extracted features are not efficient and required...
This paper presents a novel approach for improving the accuracy of existing 3D face recognition algorithms via the dimensionality reduction of the feature space. In particular, two feature selection methods based on information criteria are selected and benchmarked herein (i.e. the minimum Redundancy — Maximum Relevance (mRMR) and the Conditional Mutual Information with Nearest Neighbors estimate...
Based on linear regression techniques, we present a new supervised learning algorithm called Class-oriented Regression Embedding (CRE) for feature extraction. By minimizing the intra-class reconstruction error, CRE finds a low-dimensional subspace in which samples can be best represented as a combination of their intra-class samples. This characteristic can significantly strengthen the performance...
This paper presents a novel approach to solve the supervised dimensionality reduction problem and feature extraction by encoding an image object as a general tensor of 2-D/3-D order. In this paper a multilinear principal component analysis (MPCA) for tensor object feature extraction and then a multilinear discriminant analysis (MDA), to find the best subspaces have been proposed. It should be noted...
In the past few years, manifold learning and sparse representation have been widely used for feature extraction and dimensionality reduction. The sparse representation technique shows that one sample can be linearly recovered by the others in a data set. Based on this, sparsity preserving projections (SPP) has recently been proposed, which simply minimizes the sparse reconstructive errors among training...
Locality preserving projection (LPP) is a promising manifold learning approach for dimensionality reduction. However, it often encounters small sample size (3S) problem in face recognition tasks. To overcome this limitation, this paper proposes a discrete sine transform (DST) feature extraction approach and develops a DST-feature based LPP algorithm for face recognition. The proposed method has been...
It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition field. Face recognition databases are usually high dimensionality, especially when limited training samples are available for each subject. Traditional techniques perform dimensionality reduction are unable to solve this problem smoothly, which makes feature extraction task much difficult...
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...
Neighborhood preserving embedding (NPE) is a typical graph-based dimensionality reduction algorithm, which has been successfully applied in many practical problems such as face representation and recognition. NPE depends mainly on its underlying graph matrix which characters the local neighborhood reconstruction relationship between data points. However, the graph constructed in NPE merely utilizes...
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...
In this paper, an extended locality preserving discriminant analysis (ELPDA) method is proposed. To address the disadvantages of original locality preserving discriminant analysis (LPDA), a new locality preserving between-class scatter, which is characterized by samples and the corresponding k out-class nearest neighbors, is defined. Moreover, the small sample size problem is also avoided by solving...
Facial Expression Recognition has mostly been done on frontal or near frontal faces. However, most of the faces in real life are non-frontal. This paper deals with in-plane rotation of faces in image sequences and considers the six universal facial expressions. The proposed approach does not need to rotate the image to frontal position. FER by rotating images to frontal is sensitive to determination...
A face recognition method that based on Gabor wavelet transform and fractal is proposed, Since Gabor feature is robust to illumination and expression variations and has been successfully used in face recognition area. First, the proposed method decomposes the normalized face image by convolving the face image with multi-scale and multi-orientation Gabor filters to extract their corresponding Gabor...
Principal Component Analysis (PCA) is a wellknown and efficient technique for feature extraction and dimension reduction, which has been applied widely in community of machine learning and pattern recognition. But traditional PCA suffers from two disadvantages which restricts it's treatment of two dimensional data, like human faces, fingerprints, palmprints and other biological features which are...
One of the challenges the face recognition application is facing today is that of the high dimensionality of multivariate data. In this context, this paper proposes to compare the performance of a triumvirate combination of linear dimensionality reduction techniques namely Singular Value Decomposition (SVD) which maximizes the variance of the training vectors, Direct Fractional Linear Discriminant...
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...
In this paper, we investigate the face recognition problem via clustering of frontal face images represented in frequency domain by low frequency discrete cosine transform (DCT) coefficients. Our approach termed as class specific space model (CSSM) is based on the assumption that faces of different subjects are clustered in different low dimensional subspace of the feature space. The proposed approach...
We propose in this paper an improved manifold learning method called two-directional two-dimensional discriminant locality preserving projections, (2D)2-DLPP, for efficient image recognition. As the existing method of two-dimensional discriminant locality preserving projections (2D-DLPP) mainly relies upon the local structure information in the rows of images, we first derive an alternative 2D-DLPP...
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