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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...
This paper introduces a new collaborative feature extraction method based on projection pursuit with application to face recognition. We propose a new projection pursuit index based on the weighted sum of six state of the art indices. Using a genetic search, the hyperparameters of the proposed projection index as well as of the selected classifier were jointly optimized to improve the generalization...
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...
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...
This paper presents a new nonparametric linear feature extraction method coined geometrically intuitive marginal discriminant analysis (IMDA). Motivated by the law of cosines in trigonometry, we characterize the square local margin by a weighted difference of the square between-class distance and the square within-class distance. Based on this characterization, we design a class margin criterion which...
In this paper, two-dimensional locality preserving projections (2DLPP) was proposed to extract Gabor features for face recognition. 2DPCA is first utilized for dimensionality reduction of Gabor feature space, which is implemented directly from 2D image matrices. The objective of 2DLPP is to preserve the local structure of the image space by detecting the intrinsic manifold structure. In our method,...
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...
Recently, locality sensitive discriminant analysis (LSDA) was proposed for dimensionality reduction. As far as matrix data, such as images, they are often vectorized for LSDA algorithm to find the intrinsic manifold structure. Such a matrix-to-vector transform may cause the loss of some structural information residing in original 2D images. Firstly, this paper proposes an algorithm named two-dimensional...
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