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In this paper, a new face recognition method based on PCA (principal component analysis), LDA (linear discriminant analysis) and neural networks is proposed. This method consists of four steps: i) preprocessing, ii) dimension reduction using PCA, iii) feature extraction using LDA and iv) classification using neural network. Combination of PCA and LDA is used for improving the capability of LDA when...
In this paper, we present new self-organized networks to extract optimal features from multidimensional Gaussian data while preserving class separability. For this purpose, we introduce new adaptive algorithms for the computation of the square root of the inverse covariance matrix Sigma-1/2. Then we construct self-organized networks based on the proposed algorithms and use them for optimal feature...
In this paper, we present new adaptive linear discriminant analysis (LDA) algorithm and apply them for adaptive facial feature extraction. Adaptive nature of the proposed algorithm is advantageous for real world applications in which one confronts with a sequence of data such as online face recognition and mobile robotics. Application of the new algorithm on feature extraction from facial image sequences...
In this paper, we present a new incremental face recognition (IFR) system based on new adaptive learning algorithms and networks. We introduce new adaptive linear discriminant analysis (LDA) algorithm and related network for optimal facial feature extraction and use them to construct a new IFR system. Convergence proof of all algorithms is given using an appropriate cost function and discussing about...
Fuzzy c-mean (FCM) is a common clustering algorithm which is used for segmentation of magnetic resonance (MR) images. However in the case of noisy MR images, efficiency of this algorithm considerably reduces. Recently, researchers have been introduced two new parameters in order to improve performance of traditional FCM in the case of noisy images. New parameters are computed using artificial neural...
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