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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,...
This paper presents a novel manifold learning method, namely Directional two-dimensional neighborhood preserving embedding (Dir-2DNPE), for feature extraction. In contrast to standard NPE, Dir-2DNPE directly seeks the optimal projective vectors from the directional images without image-to-vector transformation. Moreover, Dir-2DNPE can well reserve the spatial correlations between variations of rows...
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...
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,...
Neighborhood Preserving Embedding (NPE) is an unsupervised linear dimensionality reduction technique which attempts to solve the ldquoout of samplerdquo problem in Locally Linear Embedding (LLE). This is done by introducing a linear transform matrix into LLE, and hence NPE can be perceived as a linear approximation to LLE. In this paper, we modify the original NPE for face recognition by embedding...
Feature extraction plays a fundamental role in image classification and retrieval. However, the obtained feature space is often high-dimensional and dimensionality reduction is necessary to alleviate the curse of dimensionality or reduce the computational complexity. In this paper, we propose an image classification approach based on Laplacian PCA(LPCA). The notion of LPCA is borrowed from the area...
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