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The linear discriminant analysis (LDA) is one of the most efficient supervised dimensionality reduction technique widely used in face recognition. This paper proposed a new weighted LDA to improve the performance of the discriminant analysis. Confusable pair of classes is considered as the primary goal in our objective function. The proposed technique not only improves the minimization of the within-class...
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...
Feature extraction and classification are two essential components in face recognition. Feature extraction is a process to reduce the original input high-dimensional data and reserve the crucial information. Considering the problem that the human face image is high-dimensional, dimensionality reduction (DR) methods can be employed to obtain low-dimensional data for recognition. Eigenspace-based method...
Face recognition (FR) has received significant attention as one of the most successful applications of image analysis and understanding, during the past several years and is an active yet challenging topic in computer vision applications. Also potentially will help in identifying ultra-rare and developmental disorders. Linear discriminant analysis (LDA) has been widely used for feature extraction...
In this paper, we present a novel approachfor face recognition which consists of a dimensionalityreduction of face feature vectors. The image scaling is firstlyconducted on an input face image. Then we applied the LocalBinary Pattern (LBP) operator by dividing the face imageinto non-overlapped regions. LBP histograms are extractedfrom each region and concatenated into a single one thatrepresents the...
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...
In this paper, a novel framework for facial expression recognition is proposed, which improves the conventional feature extraction technique to further exploit distinctive characters for each label. To reduce the effect from unrelated features for facial expression recognition, a denoising mechanism is introduced. After denoising, to keep the connection between expression labels and whiten features...
Lorentzian geometry is a subject of mathematics and has famous applications in physics, especially in relativity theory. This geometry has interesting features, e.g. one axis has a negative sign in metric definition (time axis). In this study, we try to apply Lorentzian geometry for feature extraction and dimensionality reduction. We use a Lorentzian Manifold (LM) for face recognition and reduce the...
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...
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...
This paper explores the use of Two-Dimensional Robust Neighborhood Discriminant Embedding (2D-RNDE) as a means to improve the performance and robustness of face recognition. 2D-RNDE is based on graph embedding framework and Fisher's criterion, it can utilize the original two-dimensional image data directly and takes into account the Individual Discriminative Factor (IDF) which is proposed to describe...
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...
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...
In face recognition, a way to enhance the discriminability is to provide effective feature representation. Dual-Tree Complex Wavelet transform (DT-CWT) provides a local multiscale description of images with good directional selectivity and shift invariance, and is robust to illumination variations and facial expression changes. In this paper, we propose a novel approach to face feature extraction...
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...
Linear discriminant analysis(LDA) is one of the most popular methods for feature extraction and dimensionality reduction, but it may encounter the so called small sample size(SSS) problem when applied to high dimensional data analysis such as face recognition. In addition, the between-class scatter matrix defined in terms of class centroids places no restrictions on individual samples, maximization...
Dimensionality reduction technique is very important to appearance-based face recognition algorithm. Compared with monochromatic face image, color face image which is composed of different color channels can provide more cues for recognition task. In this paper, a novel appearance-based recognition approach, modified local NMF based color face recognition, is proposed. Block diagonal matrix mode is...
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