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In order to overcome the instability of non-linear high dimensional facial data caused by illumination and expression, and obtain rich, effective and complementary face features, we put forward a method for face recognition by improving kernel entropy component analysis (KECA) with weighted multi-resolution. First, we use traversal algorithm to search an optimal weight for the multi-resolution face...
Face recognition is a quintessential biometric technique. It still remains challenging to accurately characterize the identity related features in face images. In this paper, we propose a novel classification method based on Kernel Fisher Discriminant Analysis using the distinctiveness of Gabor features and the robustness of ordinal measures. These parameters are derived from magnitude, phase, real...
Facial physical appearance normally have several variations which occurred due to changes in expression, illumination, occlusion, head pose, and aging. In actual, human eyes can able to justify the authenticity of a person by the use of single image per class. In this paper, a new framework is proposed for nonlinear classification of face images with only one training image per class. Histogram Equalization...
Automatic gender detection through facial features has become a critical component in the new domain of computer human observation and computer human interaction (HCI). Automatic gender detection has numerous applications in the area of recommender systems, focused advertising, security and surveillance. Detection of gender by using the facial features is done by many methods such as Gabor wavelets,...
Age invariant face recognition is an important yet less investigated problem in the face recognition community. In this paper, we empirically evaluate state-of-the-art facial feature representations for age-invariant face recognition. Three representative features including local binary pattern (LBP), Gabor wavelets and gradient orientation pyramid (GOP) were applied, followed by a principal component...
An effective face recognition method is described in the proposed paper, which is based on Gabor Wavelets and 2D Linear Discriminant Analysis (Gabor-2DLDA). Although Gabor features has been recognized as one of the most successful face representations, its huge number of features often brings about the problem of curse of dimensionality. In this paper, we use Gabor feature matrix to represent the...
This paper introduces a novel Gabor-based median maximum scatter difference (GMMSD) method for face recognition. Maximum scatter difference(MSD) is a recently proposed linear discriminative method for dimensionality reduction. Its theory is similar to linear discriminative analysis (LDA). In this paper, we investigate its extension, called median MSD, in which the within-class mean is replaced with...
In this paper, a new technique called two dimensional Gabor principle component analysis (2DGPCA) is derived and implemented for image representation and recognition. The 2DGPCA method addresses the problems of feature extraction, feature selection and classification. In this approach, the Gabor wavelets are used to extract facial features. The principle component analysis (PCA) is then applied directly...
Based on Gabor wavelets, a novel multi-scale principal component analysis and support vector machine algorithm (MsPCA-SVM) for face recognition is proposed in this paper. Firstly, the Gabor wavelets transformation results including five scales and eight directions are calculated and 40 feature matrices which are reconstructed with the same scale and the same direction transform results of the different...
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