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Extracting accurate positions of eyes, nose and mouth, is a crucial process for face recognition and facial expression recognition. Classical methods such as Active Appearance Model (AAM) use the principal component analysis to reduce the dimensionality of appearance data, and an iterative search to find facial features by minimizing an error criteria of the reduced appearance data. In this paper,...
A novel feature extraction method that utilizes nonlinear mapping from the original data space to the feature space is presented in this paper. For most practical systems, the meaningful features of a pattern class lie in a low dimensional nonlinear constraint region (manifold) within the high dimensional data space. A learning algorithm to model this nonlinear region and to project patterns to this...
Recently, local discriminant embedding (LDE) was proposed to manifold learning and pattern classification. In LDE framework, the neighbor and class of data points were used to construct the graph embedding for classification problems. From a high dimensional to a low dimensional subspace, data points of the same class maintain their intrinsic neighbor relations, whereas neighboring data points of...
Face recognition has become one of the most important research areas of pattern recognition and machine learning due to its potential applications in many fields. To effectively cope with this problem, a novel face recognition algorithm is proposed by using manifold learning and minimax probability machine. Comprehensive comparisons and extensive experiments show that the proposed algorithm achieves...
In this paper, we propose a novel supervised learning method called Global Sparse Representation Projections (GSRP) for linear dimensionality reduction. GSRP can be viewed as a combiner of sparse representation and manifold learning. But differing from the recent manifold learning methods such as Local Preserving Projections (LPP), GSRP introduces the global sparse representation information into...
A new feature extraction method based on manifold learning is proposed for face recognition in the paper; its criterion function is characterized by maximizing the difference between the nonlocal scatter and the local scatter. The novel method is called two-directional two-dimensional marginal discriminant projection ((2D)2MDP), which simultaneously works image matrix in the row direction and in the...
Feature extraction is an important step for face recognition. The capability of feature extraction directly influences the performance of face recognition. Recently, some manifold learning algorithms have drawn much attention. Among them, neighborhood preserving projections is one of the most promising feature extraction techniques. Though NPP has been applied in many fields, it has limitations to...
In this paper we present a novel appearance based approach to the problem of face pose classification. This method suggests the subject-independent pose classification of face images using bilateral filtering and wavelet transform as preprocessing and isometric projection based subspace learning for the extracting of discriminant feature vectors. Our proposed method is evaluated on a large image set...
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features. In this paper, a new manifold learning algorithm, called Uncorrelated Locality Information Projection (ULIP), to identify the underlying manifold structure of a data set. ULIP considers both the between-class scatter and the within-class scatter in the processing of manifold learning. Equivalently,...
Faces captured by surveillance cameras are often of very low resolution. This significantly deteriorates face recognition performance. Super-resolution techniques have been proposed in the past to mitigate this. This paper proposes the novel use of a Locality Preserving Projections (LPP) algorithm called Direct Locality Preserving Projections (DLPP) for super resolution of facial images, or ldquoface...
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features. In this paper, a new manifold learning algorithm, called uncorrelated maximum locality preserving projections(UMLPP), to identify the underlying manifold structure of a data set. UMLPP considers both the between-class scatter and the within-class scatter in the processing of manifold learning...
Subspace methods have been successfully applied to face recognition tasks. It is well-studied in both unsupervised learning and supervised learning, such as Eigenface and Fisherface. In practice, besides abundant unlabeled examples, domain knowledge in the form of pairwise constraints is commonly available, which specifies whether a pair of instances belong to the same class or different classes....
In this paper, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM) is developed for dimensionality reduction. The proposed algorithm can be viewed as a linear approximation of multi-manifolds based learning approach, which takes the local geometry and manifold labels into account. After the local scatters have been characterized, the proposed method focuses...
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