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This paper presents an empirical investigation of two sparse random projections which correspond to extraction of vertical and horizontal features from a face image for identity verification. In order to enhance the performance of each projection, the matching scores of both directional features are fused via a total error rate minimization. The BERC face database is used for evaluating the effectiveness...
Because of the high data dimensionality of hyperspectral data, it is somehow difficult to directly apply hyperspectral images in classification and target detection. A fusion method of hyperspectral images based on feature images extraction and contourlet analysis is proposed. The algorithm firstly extracts feature images using subspace partition and principal components analysis (PCA), then these...
A feature fusion algorithm with application to facial expression recognition is presented. Firstly, the brows, eyes and mouth areas are segmented from the facial expression images, and are computed with Higher-order Local Auto-Correlation (HLAC) method, and the Weighted Principal Component Analysis (WPCA) is used to reduce dimensions secondly, in which the weights values are obtained according to...
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