Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A new method of feature fusion and its application in image recognition
Pattern Recognition
Face recognition based on PCA/KPCA plus CCA
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
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Canonical correlation analysis (CCA) can extract more discriminative features by utilizing class labels, especially the ones that can reflect the sample distribution appropriately. In this paper, a new fuzzy approach for handling class labels in the form of fuzzy membership degrees is proposed. We elaborately design a novel fuzzy membership function to represent the distribution of image samples. These fuzzy class labels promote the classification performances of CCA and kernel CCA (KCCA) through incorporating distribution information into the process of feature extraction. Comprehensive experimental results on face recognition demonstrate the effectiveness and feasibility of the proposed method.