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Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, kernel-based feature extraction and multiple feature fusion. Specifically, we make three main...
This paper introduces a framework that employs the Fisher linear discriminant model (FLDM) and classifier (FLDC) on integrated facial appearance and facial expression features. The principal component analysis (PCA) is firstly applied for dimensionality reduction. The normalized fusion method is then applied to the reduced lower dimensional subspaces of these two features. Finally, the FLDM is used...
In the paper, we propose a new method for ear recognition. Firstly, we extract global features using kernel principal component analysis (KPCA) technique and extract local features using independent component analysis (ICA) technique. Then we establish a correlation criterion function between two groups of feature vectors, extract their canonical correlation features according to this criterion, and...
In this paper, some of face recognition algorithms are compared, which are based on Two-dimensional principal component analysis (2DPCA), singular value decomposition (SVD) and the fusion of 2DPCA and SVD. The experiment results, by means of ORL and Yale face database, indicate that the algorithm combined 2DPCA and SVD-U has a higher recognition rate, 87% and 95.9%, respectively. And it can have better...
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