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Face detection has become a hot topic in the field of pattern recognition and computer vision. This paper proposed an improved spatial pattern clustering algorithm, each pixel in a color image will be taken as a pattern which implies not only its feature value but its spatial information. So, the clustering of all pixels of an image is converted to the clustering of the corresponding patterns. The...
General morphological correlation(GMC) is proposed. Two kinds of modified phase-encoded general morphological correlation are proposed. The gray-scale image is decomposed into a set of binary image slices in certain decomposition method. One situation is that the joint power spectrum of each pair is binarized or phase-encoded and then summed, another situation is that the summation of the joint power...
This paper proposes a three-stage scheme to obtain real-time and reliable face detection in intersecting monitoring. The proposed three-stage scheme is mainly based on skin color and facial features. In the first stage scheme, skin-color model is used to obtain skin regions. The second stage scheme uses face template measure to obtain face candidates. Finally, facial features are measured to detect...
There is a growing interest in subspace discriminative feature extraction techniques based on tensor (multilinear) representation, which encodes an image object as a general tensor of second or even higher order. However, on one hand the computational convergence of its iterative algorithms is not guaranteed, on the other these methods are impractical for real-time applications for large training...
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...
A nonlinear DCT discriminant feature extraction approach for face recognition is proposed. First, we analyze the nonlinear discriminabilities of DCT frequency bands and select appropriate bands. Second, we extract nonlinear discriminant features from the selected DCT bands by presenting a new kernel discriminant method, i.e. generalized kernel discriminative common vector (KDCV) method. The experimental...
A novel nonlinear feature extraction and recognition approach which is based on improved 2D Fisherface plus Kernel discriminant analysis is proposed. We provide an improved 2D Fisherface method that designs a new strategy to select appropriate 2D principal components and discriminant vectors, then we use 2D features to perform the Kernel discriminant analysis. The nearest neighbor classifier with...
This paper proposes a two-phase algorithm of image projection discriminant analysis. The new discriminant method is composed of feature extraction by on maximum margin criterion (MMC) and Fisher discriminant analysis (FDA). The algorithm includes two stages: firstly, the feature extraction based on maximum margin criterion (MMC) is employed to condense the dimension of image matrix; Then Fisher discriminant...
Unsupervised discriminant projection (UDP) has a good effect on face recognition problem, but it has not made full use of the training samples' class information that is useful for classification. Linear discrimination analysis (LDA) is a classical face recognition method. It is effective for classification, but it can not discover the samples' nonlinear structure. This paper develops a manifold-based...
In this paper, we propose a feature extraction method called two dimension locally principal component analysis (2DLPCA) for face recognition, which is based directly image matrix rather than 1D image vectors. 2DLPCA seeks to discover the intrinsic image local structure. This local structure may contain useful information for discrimination. Experimental results on ORL face database show the effectiveness...
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